This research proposes the use of hybrid machine learning methods to mimic dye removal efficiency. Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. This study focused on the relationships between the initial dye concentrations of Fast Green, Eosin Y, and Quinine Yellow dyes, their initial pH, ACMOF adsorbent dosage (activated carbons: metal‒organic frameworks), and sonication time as input variables, with the removal percentage as the output data. The analysis emphasized the correlation between the inputs and outputs, resulting in the generation of four scenarios: 4 inputs, 3 inputs, 2 inputs, and 1 input. The correlation analysis revealed a weak input‒output relationship and the presence of outliers in the data. The use of advanced models, such as XGBoost, improved model performance and accurately predicted dye removal efficiency. The models performed well across different input scenarios, demonstrating their reliability and effectiveness. The results also revealed the importance of data preprocessing techniques in improving the structure and relationships within the data. The DE_XGBoost model outperforms all the other methods in terms of R2 (R2 values of 0.977, 0.958, 0.924, and 0.997 for 4-input, 3-input, 2-input, and 1-input, respectively), demonstrating its DE effectiveness in generalizing the model and enhancing its predictivity. This research contributes to the development of more efficient techniques for dye removal and environmental pollution mitigation, addressing the challenges of traditional testing methods. These findings have implications for industries that use dyes and can help mitigate the environmental pollution caused by dye effluents.

Groundwater prediction in data-scarce and environmentally sensitive regions presents a persistent challenge due to limited observational data, spatial heterogeneity, and the nonlinear nature of hydrogeological processes. In this study, we propose HydroPredictor, a hybrid machine learning framework that integrates the categorical handling efficiency of CatBoost with the nonlinear feature learning capacity of a regularized Multi-Layer Perceptron (MLP). The model was trained on a geo- referenced dataset of 315 samples from the Feija Basin in southeastern Morocco, incorporating ten environmental predictors such as elevation, rainfall, soil permeability, NDVI, and topographic wetness index. The pipeline includes Optuna-based hyperparameter optimization and 5-fold cross-validation to ensure robustness and generalization. HydroPredictor achieved a testing accuracy of 89.23%, with an F1-score of 0.8937 and Area Under the Curve (AUC) values exceeding 0.90 across all groundwater potential classes. Statistical validation using the Friedman and Wilcoxon signed-rank tests (p < 0.05) confirmed its significant outperformance over conventional models, including Random Forest, Support Vector Machine (SVM), and standalone MLP. Furthermore, HydroPredictor demonstrated superior generalization compared to prior models in the literature (e.g., RF-SSA: AUC = 0.840; GBDT: AUC = 0.88), while maintaining minimal overfitting (∆Accuracy = 0.35%). By combining interpretable tree-based embeddings with deep neural representations, HydroPredictor provides a robust and scalable solution for groundwater classification in data-limited settings, offering a reproducible and operationally relevant tool for sustainable groundwater resource management under climatic and environmental uncertainty.

Microplastic contamination of terrestrial and aquatic environment has gained immense research attention due to their potential ecotoxicity and biomagnification property when enterer into food chain. Heterogenous nature of microplastics coupled with their ability to combine with other emerging pollutants have increased the severity of this crisis. Existing detection methods often fails to accurately quantify the amount of microplastic components present in environmental and biological samples. Thus, a great deal of research gap always exists in our current understanding about microplastics including the limitations in screening, detection and mitigation. This review work presents a comprehensive out look on the impact of microplastics on both terrestrial and aquatic environment. Furthermore, an in-depth discussion on various microplastic detection techniques recently used for microplastic quantification along with their significance and limitations is summarised in this review. The review also elaborates various physical, chemical and biological methods used for the mitigation of microplastics from environmental samples.

The objective of the present study is to investigate the application of artificial intelligence tools in dephenolization of simulated wastewater. The adsorbent was waste corncob activated and impregnated with tetraoxophosphate V acid (H3PO4). The adsorbent was characterized via SEM and FTIR analysis. Artificial intelligence models such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) were used in modeling the dephenolization process. Genetic algorithm (GA) was used to optimize the best models. The results indicated that H3PO4-assisted impregnation enhanced the adsorptive dephenolization capability of the adsorbent. The BET surface area analysis of the modified adsorbent showed a surface area of 903.7 m2/g, average pore width of 5.55 nm and micropore volume of 0.389 cm2/g. SEM micro-graph showed that the modified activated carbon has asymmetrical and interspatial pores which indicated desirable sorption properties of the adsorbent. The correlation coefficient (R2) of the ANFIS, ANN and RSM models were 0.9998, 0.9998, and 0.9430 respectively while the Adjusted R2 were 0.9339, 0.9998, and 0.9998 respectively. This implied that ANN and ANFIS models were more significant than RSM. Further statistical analysis suggested that ANFIS and ANN have almost equal capability in modeling the dephenolization process. The ANN-GA and ANFIS-GA optimization gave maximum percentage of phenol removal as 92.44% and 92.34% respectively under optimum process conditions. The maximum adsorption capacity of 121 mg/g obtained from the GA optimization was comparably higher than most reported works. The point of zero charge was 5.65 while the regeneration with 0.2M NaOH showed best adsorbent reusable capacity. These results suggest that these waste corn cob activated carbon can be utilized as a high performance and efficient adsorbent in dephenolization of waste water. This is in addition to the reduction of the huge environmental waste associated with corn cobs. Furthermore there is no harmful environmental impact and no generation of toxic by-products during the adsorption process.

This review explores the multifaceted benefits of Myrica species, commonly known as bayberry, highlighting their therapeutic, nutritional, and environmental value. The fruit of the Myrica tree is very good for you. It’s full of important nutrients and bioactive compounds, like myricetin, that help your immune system and your overall health. Tree bark, in addition to fruit, contains essential oils, and locals use the wood for construction. Various species of Myrica, including Myrica esculenta (Myrica Nagi), which is found in India, are distributed across several countries, including China, Nepal, Japan, Singapore, and Malaysia. Although the industrial uses of bayberry have received limited research, its pharmacological and medicinal properties have been the subject of extensive studies. The high nutritional content of fruit includes iron, magnesium, sodium, potassium, calcium, and copper. Additionally, it contains bioactive compounds such as tannins, flavonoids, volatile compounds, saponins, and phenolic acids. his study looks at how research trends changed from 2014 to 2024 using bibliographic tools that discuss the fruit’s uses and its health, nutrition, and environmental benefits. The study also maps the species’ distributions around the world. Despite its numerous benefits, bayberry faces threats from overexploitation and urbanization, leading to its decline. There is a pressing need to explore and develop sustainable uses and byproducts of these wild fruits to preserve their value and ensure their availability for future generations.

This study investigates the use of novel flattened-end nylon fibres (FENF) as reinforcement in concrete to improve its mechanical properties. The research addresses inadequate circumferential bonding between macro synthetic fibres and concrete matrix, which can lead to fibre slippage or failure. Through experiments involving 19 concrete mixes with varying fibre dosages (0.5%, 1% and 1.5%), aspect ratios (35, 55 and 75), and shapes (straight and flattened-end), the study examines the impact of FENF on concrete workability and mechanical strengths. The mechanical strength tests illustrate the significance of fibre dosage, aspect ratio and especially the shape as the compressive, split-tensile strength and flexural strengths of the FENF concrete are respectively showing an increase in strength of up to 10.3%, 25.1% and 26.1% when compared with conventional concrete. Similarly, the straight nylon fibre-reinforced concrete also achieved comparable strength increments up to 11.8%, 13.9% and 15.9% respectively for compressive, split-tensile and flexural strengths. This indicates that the positive effect of fibre shape on circumferential bonding helped the better performance of the FENF in split-tensile and flexural strengths. Further, the statistical methods, including regression analysis, Principal Components Analysis, and Response Surface Methodology, are employed to analyse the complex relationships between fibre characteristics and identify optimal fibre configurations. Using the Scanning Electron Microscope (SEM) the microstructural view has been studied to evaluate the interaction between FENF and the concrete matrix.

Morocco’s oases, critical agroecological systems in arid regions, face escalating water scarcity due to climate variability, groundwater depletion, and a historic decline in palm groves from 15 million to 4 million trees over the past century. This study introduces a machine learning (ML)-based precipitation forecasting framework to enhance water resource management in four semi-arid Moroccan regions: Errachidia, Figuig, Tata, and Zagora. Leveraging a 1981–2025 dekadal rainfall dataset from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2), we developed and compared four ML models: XGBoost, CatBoost, Long Short-Term Memory (LSTM), and Temporal Convolutional Network (TCN). CatBoost emerged as the most effective, achieving a testing  of 0.9818 and a mean squared error (MSE) of 0.5430 mm on historical data (2019–2025), and a fine-tuned 90-day forecast (March 5–June 3, 2025) with  of  and MSE of 0.3364 mm. Historical trends revealed declining precipitation post-2015, underscoring the need for predictive tools. These findings demonstrate CatBoost’s superior ability to capture nonlinear rainfall dynamics, offering a scalable solution for climate-resilient water management in water-scarce regions. However, challenges such as data sparsity and model interpretability highlight the need for enhanced observational networks and explainable AI approaches to maximize practical adoption.

This study presents an in-depth analysis of machine learning (ML) techniques for predicting water quality index and water quality classification using a dataset containing water quality metrics such as temperature, specific conductance, salinity, dissolved oxygen, depth, pH, and turbidity from multiple monitoring stations. Data preprocessing included imputation for missing values, feature scaling, and categorical encoding, ensuring balanced input features. This research evaluated artificial neural networks, decision trees, support vector machines, random forests, XGBoost, and long short-term memory (LSTM) networks. Results demonstrate that XGBoost and LSTM significantly outperformed other models, with XGBoost achieving an accuracy range of 99.07–99.99% and LSTM attaining an R2 of 0.9999. Compared with prior studies, our approach enhances predictive accuracy and robustness, showcasing advanced generalization capabilities. The proposed models exhibit significant improvements over traditional methods in handling complex, multivariate water quality data, positioning them as promising tools for water quality prediction and environmental management. These findings underscore the potential of ML for developing reliable, scalable water quality monitoring solutions, providing valuable insights for policymakers and environmental managers dedicated to sustainable water resource management.

This study investigates the use of novel flattened-end nylon fibres (FENF) as reinforcement in concrete to improve its mechanical properties. The research addresses inadequate circumferential bonding between macro synthetic fibres and concrete matrix, which can lead to fibre slippage or failure. Through experiments involving 19 concrete mixes with varying fibre dosages (0.5%, 1% and 1.5%), aspect ratios (35, 55 and 75), and shapes (straight and flattened-end), the study examines the impact of FENF on concrete workability and mechanical strengths. The mechanical strength tests illustrate the significance of fibre dosage, aspect ratio and especially the shape as the compressive, split-tensile strength and flexural strengths of the FENF concrete are respectively showing an increase in strength of up to 10.3%, 25.1% and 26.1% when compared with conventional concrete. Similarly, the straight nylon fibre-reinforced concrete also achieved comparable strength increments up to 11.8%, 13.9% and 15.9% respectively for compressive, split-tensile and flexural strengths. This indicates that the positive effect of fibre shape on circumferential bonding helped the better performance of the FENF in split-tensile and flexural strengths. Further, the statistical methods, including regression analysis, Principal Components Analysis, and Response Surface Methodology, are employed to analyse the complex relationships between fibre characteristics and identify optimal fibre configurations. Using the Scanning Electron Microscope (SEM) the microstructural view has been studied to evaluate the interaction between FENF and the concrete matrix.

The primary objective is to select an appropriate aerator that maximizes the benefits while minimizing the operational costs of the aeration system in aquaculture operations. The choice of suitable aerators significantly impacts the cost of aquaculture operations. Therefore, this study focuses on the economic analysis of the paddle wheel aerator (PWA) and its modified counterpart, the spiral aerator (SA). The efficiency of the PWA and SA was assessed with respect to rotational speed (N), based on the efficiency a comparative economic analysis was conducted. The economics of aerators depends on the various pond sizes, initial dissolved oxygen (DO), and aerators running hours. Therefore, the total aeration cost for the chosen aerators was determined at various pond volumes (100, 200, 300, 700, 1000, 5000, and 10,000 m3 with depth of water 1.0 m) and initial DO of pond water denoted as CP = 1 to 4 mg/L. According to market rates, the primary cost for PWA was ₹32,000, while that for SA was ₹42,000. From the findings, pond sizes in the range of 100 to 10,000 m3 PWA are more economical than SA. Therefore, a new selection method of economically feasible aerators in aquaculture pond was developed in this study.

This study introduces a coalesce forecasting model tailored for flood-prone regions, specifically focusing on Bihar, India. Research has revealed significant disparities in rainfall patterns across various zones such as Tirhut, Patna, and Munger zones experiencing greater mean rainfall than Bhagalpur and Kosi. To evaluate the forecasting capabilities, coalescing methods were applied which includes the autoregressive integrated moving average (ARIMA), exponential smoothing state space (ETS), neural network autoregressive (NNAR), and seasonal-trend decomposition. Moreover, Loess (STL) methods, and trigonometric seasonality, Box‒Cox transformation, ARMA errors, and trend and seasonal components (TBATS) were also employed to contrast the benchmark models such as the seasonal naïve, naïve, and mean methods. These methods were evaluated using error evaluators such as residual error, root mean square error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE), and autocorrelation of errors at lag 1 (ACF1) to determine the performance of these techniques. Additionally, statistical tests, such as the Box–Pierce and Box–Ljung tests, supported these findings. Among the error evaluators and forecasting models, the ETS and NNAR models remain the top choices for Saran-Tirhut-Bhagalpur and Munger-Magadh-Kosi, respectively, effectively capturing rainfall patterns and minimizing residual errors, as indicated by low RMSE values. Moreover, ARIMA and TBATS remain the top choices for Patna, Purnia and Darbhanga, respectively, followed by ETS model. In addition, the STL model secured the second position for Saran, Tirhut, Bhagalpur, and Purnia zones. This research emphasizes the importance of understanding regional rainfall dynamics for effective flood risk management and climate adaptation strategies. This study provides valuable tools for water resource management and agricultural planning in Bihar amidst climate variability challenges. It advocates for rainfall trend analysis followed by forecasting to achieve more precise water resource management and planning.

Artificial intelligence (AI) offers innovative and efficient solutions to contemporary challenges in sustainable aquaculture. Machine learning (ML) and deep learning (DL) are integral components of smart aquaculture, driving significant advancements in the field. The integration of AI with ML, and DL technologies is transforming traditional aquaculture practices by enhancing operational efficiency, optimizing fish health management, improving environmental conditions, monitoring water quality and supporting advanced decision-making processes. This review highlights the latest applications of AI, including ML, and DL in aquaculture, emphasizing their roles in real-time water quality monitoring, disease detection, and automated estimation of fish biomass etc. Key techniques, including predictive modeling, image and video processing, and sensor data integration, are enabling these breakthroughs. Moreover, DL algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have emerged as powerful tools for processing complex data and predicting critical events within aquaculture systems. Despite the notable progress, challenges such as the need for large, labeled datasets, high computational costs, and issues related to model interpretability continue to limit broader adoption. The current review aims to offer researchers and practitioners with a comprehensive overview of AI and its subfields such as ML and DL applications in smart aquaculture, discussing both the opportunities and challenges while suggesting future research directions to overcome existing limitations and expand AI-driven innovations in the industry.

The aquaculture industry will be crucial in helping the world’s food supply and keep up with the demand. Aquaculture, like agriculture, must expand and develop sustainably in all the countries to keep up with the rising demand for food. To this end, the aquaculture industry has forged new scientific and technological frontiers in pursuit of long-term food security. Among these is organic aquaculture, whose distinctive protocol has great potential to advance aquaculture. Organic aquaculture is being explored for multiple reasons, encompassing the aim to minimize environmental footprints, fulfill escalating consumer appetites for seafood, and contend within the industry this paper endeavors to address the gaps in current literature by offering an exhaustive overview of various aspects of organic aquaculture. This encompasses its regulation, production methods, food quality, environmental impact, economic viability, as well as socioeconomic and marketing aspects. It is necessary to acquire more knowledge about organic farming techniques before switching over to organic aquaculture on a large scale. Organic regulation, production, food quality, economic performance, and social and marketing issues are at the top. This review found that consumers lack understanding of organic principles, and regulations are inconsistently applied. However, organic aquaculture promotes social equality by protecting producers’ rights to work without discrimination based on gender, race, or sexual orientation factors that ultimately boost the industry’s popularity. Organic aquaculture viability varies depending on factors like feed costs, fixed expenses, and the premium pricing sensitivity, making it unfeasible for certain species. However, from both societal and economic standpoints, organic aquaculture appears most suitable for implementation in developing nations.

The pervasive presence of microplastics (MPs) in the environment has raised significant concerns about their infiltration into the human food chain. In current review, the occurrence and distribution of MPs in various food matrices such as seafood, drinking water, fruits, vegetables, and beverages are discussed along with their potential routes of MPs entry into the human food chain. The toxicity of MPs on human health and different organs are discussed in brief. Current technological advancement and green analytical methods for the detection of MPs in food samples are compared, discussing their advantages and limitations. Green analytical methods, including stereomicroscopy, Fourier Transform Infrared spectroscopy, Raman spectroscopy, and enzymatic digestion, are evaluated for their efficacy and environmental impact. The Analytical Eco-Scale is used to assess the greenness of these methods. Challenges associated with MPs detection in food, such as complex food matrices, pretreatment methods, and variability in MPs concentrations, are addressed.

This study aims to better understand the time series forecasting of Aglar and Paligaad rivers' discharge (which has a significant impact on the Himalayan river) using advanced time series methods such as Holt-Winters (HW) additive method, Simple exponential smoothing (SES), and Non-seasonal auto-regressive integrated moving average (ARIMA) models. This study used antecedent discharge information to forecast the next event. Comprehensive statistical examinations were conducted and analyzed. The highly stochastic nature of these river discharge trends adds complexity to the forecasting efforts and requires sophisticated modeling techniques that are capable of capturing and interpreting such variability accurately. The models proposed in the current study provide a reliable forecast for the next 15 months using 31 months of recorded river discharge data. The forecast analysis shows that both the HW and non-seasonal ARIMA model results indicate exponential decay for the end of 2016 and early 2017. The HW model shows the best performance in long-term forecasting, indicating a sharp increase in spring and a small increase in discharge during fall months. However, for short-term forecasting, the non-ARIMA model should show more promising results. The results show that the proposed methodologies substantially improve the forecast accuracy of discharge for all consecutive months in perennial rivers. While the study presents promising results for forecasting the Aglar and Paligaad rivers' discharge, generalizing these findings to other river systems or different geographical regions may be problematic due to varying hydrological characteristics and environmental conditions, which may need further study.

River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000–2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input parameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication.

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi; and for minimum number of inputs i.e four, are selected for  PhuongHung, CanTho, and SaPa city; that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval.

This article provides a scientometric overview of the research on water, environment, and sustainable development goals (SDGs). The focus is on water-related SDGs, including water quality and pollution sources, water-related diseases, access to sanitation and hygiene, and climate change. The study is based on a review of research articles published in major scientific databases. The paper examines the volume of research, the most influential authors and publications, the collaboration networks, and the keywords used in the research. The results highlight the growing interest in water-related research and the increasing interdisciplinary nature of the research. The analysis shows that water quality and pollution sources are the most researched topics in the water sector, followed by water-climate changes. Water-related diseases, access to sanitation and hygiene, and the role of water in urbanization are also significant areas of research. The study reveals the significant contribution of interdisciplinary water-related research, as evidenced by the increasing collaboration networks among authors from different disciplines. This paper also explores the role of water in the green economy, energy, food security, and agriculture. It reveals that the research in these areas is growing, and there is a need for interdisciplinary research to address the complex interrelationships between water and these sectors. The findings of this study have implications for policy development and research funding in the water sector. This article concludes by highlighting the need for more research on the water-related SDGs and the importance of interdisciplinary research in addressing water-related challenges.

This chapter provides an overview of the current state-of-the-art and future directions of carbon nanotubes (CNTs) application in water and wastewater treatment. The significant growth of CNT applications in the recent 5 years is highlighted by conducting detailed bibliometric analysis in various aspects. To draw major outcomes from the published works, a methodological framework consisting of search queries run through a citation database (Scopus) is implemented. The publication trends analysis shows the number of articles published over the last 5 years as well as reviews the geographic distribution of publications, top authors, institutions, and journals in the field. Further, the citation analysis section highlights the number of citations received by articles on CNTs for water and wastewater treatment and identifies influential articles and authors in the field. This approach also identifies collaborative research groups and key researchers in the field. Finally, the challenges and limitations of using CNTs in this field are thoroughly discussed, and the current state-of-the-art and future directions are reviewed. This work will serve as a tool to identify current trends and future directions for researchers willing to take up research in the CNT domain.

The growth of conventional aquaculture has created environmental issues due to excessive feeding, low dissolved oxygen level etc into the water body. Organic aquaculture is a recent development as a solution to these issues. It is a clean alternative to reduce pollution and to produce safer consumable food. In the past few decades, recirculating aquaculture systems (RAS) were introduced to maintain pond water quality through lesser water exchange, by focusing on water reuse after treatment. In this study, the technical viability of RAS was analyzed for introduction in organic aquaculture systems. Indian major carps (IMCs) were cultured through conventional methods in the three tanks of conventional system, while the same species was grown organically in another three tanks in organic system. The stocking density and physical conditions were kept same for both cultured systems. The RAS consisted of fish culture tank with an average volume of each tank was 165 m3, a screen filter, foam fractionator and trickling filter. The various water quality parameters, i.e., solid size distribution in water, and removal efficiencies in biological treatment of both systems were compared. The present study, particle size distribution of solids in water body was measured by filtration, followed by weighing of dry residue solids. Pore sizes of 1000μ, 100μ, 20μ and 3μ were employed for filtration. For organic tanks, a majority of solids are of size between 1 mm and 100 μm. The larger sized particles (> 30 μm) constitute nearly 70 % of the solids and the trait differs from the generalized conclusions of that a major part of sediments will be of sizes less than 20 μm. For the conventional tanks, majority of solids are of size between 3 mm and 20 μm and TAN shows higher removal efficiencies for effluents from organic system rather than conventional system. The results showed that organic aquaculture causes lesser pollution load per weight of fish. The particle size distribution of organic water was better compatible to screening and sedimentation than conventional water. Also the filtration efficiencies in nitrifying trickling filters of both water bodies were comparable, with that of organic water slightly on the higher side. Thus recirculating water treatment systems are introduced in organic aquaculture.

Precipitation played a vital role in the landslide events, water cycle, irrigation management, agriculture yield. The major factor is the extreme weather such as temperature played a vital role in it. This study applied climatological data (30 years of span) of two different geolocation at Vietnam, and applied time series analysis, Holt-Winters Seasonal Additive Forecasting model. The highest Precipitation i.e. 260.83 and 112.96 mm in the month of September and November for Danang and Pleiku, respectively; and total sum of 66550.51 and 34118.38 mm over the period for Danang and Pleiku, respectively. Danang is with higher Precipitation in lesser number of events, and Pleiku is with lower Precipitation though more number of events. Holt-Winters Seasonal Additive Forecasting method revealed the potential forecasting method for the different statistical characteristics data set. Lag plot showed the higher positive as well as the negative lap quartile. Residual error plot present to support the applied model feasibility. Weakness of the study and the future objective of the study drafted.

A wide range of dyes are being disposed in water bodies from several industrial runoff and the quantity is rapidly increasing over the years. From an environmental safety point of view, it is urgent to improve the removal process of dyes. It is important to understand the stochastic and highly redundant behavior of the process of dye removal (DR) in wastewater treatment. This leads to better utilization of Machine Learning (ML) models for both optimization as well as prediction of the DR process efficiency. In this review, 200 papers (Years, 2006–2021) have been systematically reviewed from the Web of Science and Scopus-indexed journals, covering a total of 84 journals. All applied ML models have been thoroughly studied in the review and analyzed in terms of their architecture setup, hyper-parameters selection, performance, advantages, and disadvantages. A wide range of optimization methods for hyper-parameters tuning were analyzed and discussed scientifically. Explicit information about the data sizes, splitting structure for training-validation-testing along with input and output selection approaches have been logically reviewed and discussed. Data availability, transparency, and reusability have been reported adequately. Various software for data-driven modeling have been discussed with their pros and cons. Trends in statistical evaluators (among about 60 types) have been discussed with their pros and cons including their sensitivity with the data fluctuations. Moreover, the most popular performance metrics have reported. In addition, the DR mechanism has reviewed and discussed inclusively. Extensive media used for the decolorization were discussed thoroughly, including their physical and chemical characteristics, along with feasibility and equilibrium data based on Langmuir model. The cost of the applied media in the decolorization process reported adequately. Finally, the research gap and future road map of the next 5 years, which bridge the gap of the domain are scientifically drafted along with the limitations. This critical review not only provides the appraisal of growth of DR research integrated with ML in the last couple of decades but also scouts the potential studies where all experimental, chemical and modeling processes should be taken under consideration.

Foundation plays a vital role in weight transfer from the superstructure to substructure. However, foundation characteristics such as pile group, piled raft, and footing remain unfolded due to their highly non-linear behaviour in different soil types. Bibliography analysis using VOSvierwer algorithm supported the significance of the research. Hence, this study investigates the load-bearing capacity of different types of foundations, including footings, pile groups, and piled rafts, by analyzing experimental data using finite element tools such as PLAXIS 2D and GEO5. The analysis involves examining the impact of various factors such as the influence of surcharge and the effect of different soil types on the load-bearing capabilities of the different types of foundation. For footing, parametric investigations using PLAXIS 2D are conducted to explore deformational changes. Pile groups are analyzed using GEO5 to assess their factor of safety (FOS.) and settling under various criteria, such as pile length and soil type. The study also provides insight into selecting the right type of foundation for civil engineering practice. Findings showed that different soil types have varying deformational behaviours under high loads with sandy soil having less horizontal deformation than clayey soil. Also, it was observed that increasing the pile thickness by 50% resulted in a reduction of 13.88% in settlement and an improvement of 16.66% in the FOS. In conclusion, this study highlights the importance of professionalism, exceptional talent, and outstanding decision-making when assessing the load-bearing capabilities of various foundation types for building structures.

Climate change effect can be observed around the globe but the most devastation is faced by economically weak and farmers in India. Kosi-Ganga-River-Creek area has witnessed frequent floods and heavy rainfall over the years. The study area is the creek where Kosi and Ganga river joins together in the Katihar district of Bihar, India. Two variables, total daily precipitation (PTot) and max daily air temperature (Tmax) (remote sensing climatological data) were fetched from ERA5 dataset using Google Earth Engine Coder to assess the climate change in the study area. The data shows stochastically fitting in further forecasting methods is as important to conduct as settings the approach reliability. This study applied exclusively time series analysis (such as decomposing a time series, seasonal subseries, and autocorrelation function (ACF) and lag time series) along with descriptive statistical analysis for both parameters of the dataset. The study found the changes in Tmax over the 30-year time period show significant variability in temperature. Tmax peaked during the year of 1990, 1995, 1998, 2004, 2008, 2012 and 2014 whereas a drop in Tmax before and after such rise was observed in the series pattern. The exponential increase in the seasonal monthly precipitation (Ptot) also correlates with the temperature increase. However, the increase is more during non-monsoon seasons like January, February and March. Although significant reduction in Ptot can be observed during May, June, July, August and September. The changes in Ptot and Tmax have caused severe damage to the agriculture and economy of the area. Thus it is essential to study climate change and forecast the probable changes in future along with other climatological conditions to mitigate the extreme weather effect. Without proper study, monitoring, assessment and management policies in Bihar will most likely continue to suffer due to agricultural losses, lively hood, life, economic losses and infrastructure.

As a result of global warming, water scarcity has become a growing concern in many parts of the world. The textile industry is one of the largest water-consumer industries that discharge untreated or partially treated wastewater into water bodies. Therefore, it is necessary to consider the wastewater quantity generated by textile industries to investigate how it is recovered and recycled. For this purpose, applying near and/or zero liquid discharge (ZLD) in the textile industry wastewater treatment is a strategic wastewater management option. Although the process is costly, technological advancements in recovering the water and other salts can pave the way for economic feasibility. This study encompasses the techno-economic analysis of textile wastewater treatment to achieve ZLD. The technologies already in use are discussed in terms of performance, cost, and limitations. An efficient pre-treatment of textile wastewater treatment could improve water recovery and decrease the operating cost of ZLD. We also discussed the ZLD applications for textile wastewater treatment, their problems, and their cost-benefit analysis.

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R2) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure.

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and Tavg oC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households in Ambo city in the Oromia region of Ethiopians were used for this purpose. The results revealed (i) 64% of the family preferred multiple water sources (i.e., public tap and river water), (ii) the water was collected females in 92% of the households, and (iii) majority of people preferred bathing and laundering in the river (support = 32% and confidence = 87%). Direct utilization of river water is not a preferable choice for the user since it may lead to severe health issues and cause water pollution from bathing and laundering. Education and monthly income have a significant impact on the choices of water sources. Local management authorities can improve sanitation and public health management using the results obtained in the study. The paper only gives a glimpse of the important factors that should be considered for improving the way of life for the underdeveloped areas of the world using advanced machine learning techniques.

Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916.

Soil temperature (ST) is an essential catchment property strongly influenced by air temperature (Ta). ST is also the key factor in sustainable agricultural developments, so researchers are still motivated to develop robust machine learning (ML) models to predict ST more reliably. Four different ML models, utilizing the standalone algorithms (i.e., artificial neural networks: ‘ANN’ and co-active neuro-fuzzy inference systems: ‘CANFIS’) and complementary algorithms (i.e., wavelet transformation combined with ANN: ‘WANN’ and wavelet transformation combined with CANFIS: ‘WCANFIS’) were developed to predict the ST at six meteorological stations incorporating a wide range of climatic features to improve the overall performance. The study has utilized data over the period 2000–2010, collected at 12 locations in Iran. In the first phase of this research, the effects of climate variability on the changes in ST at different depths (i.e., 5, 10, 20, 30, 50 and 100 cm) were explored using air temperature as the exploratory and ST as the response variable. Assessing the performance of the predictive models used in ST prediction, the results indicated good predictive capability of the WCANFIS model, thus, advocating its potential utility in ST prediction problems, especially over diverse climatic regions. This study has also ascertained that the minimum and the maximum predictive errors were encountered at a depth of about 20 cm and 100 cm, respectively. The assessment of climatic features based on air temperature datasets on the performance of the models indicated the highest efficacy demonstrated by the ANN model for the case A–C–W climate type (i.e., a moist climate regime: Arid, temperature regime in winter: Cool, and temperature regime in summer: Warm), in comparison with the PH–C–W climate type (moist regime: Per-humid) for the other best ML models (i.e., WANN, WCANFIS and CANFIS). The order of the model accuracies based on the root mean square error (RMSE) can be ranked with error values of as: WCANFIS = 0.43 °C, ANN = 0.69 °C, CANFIS = 2.16 °C and WANN = 2.31 °C, demonstrating the wavelet-based CANFIS model to exceed the performance of the counterpart comparative models. The present study provides evidence of successfully developing new ML models, improved through wavelet transform for effective feature extraction, and the importance of such hybrid models that have practical implications in studying soil temperature based on air temperature feature inputs in diverse climatic conditions. The outcomes of this study are expected to support key decisions in sustainable agriculture and other related areas where soil health, based on air temperature changes, needs to be monitored or predicted.

Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9–5 parameters without losing their learned information over the models’ training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost–SVM, XGBoost–ANN, XGBoost–Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost–Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI models, and lack in learning/technology transfer. This study explores the extreme gradient boosting (XGBoost) model as a superior SuperLearning (SL) algorithms for Pb prediction. The proposed model was examined using historical data at the Bramble and Deception Bay (BB and DB) stations, Australia. The model was trained at one of the stations and transferred to a cross-station and vice versa. XGBoost showed higher reliability with less declination in (R2: coefficient of determination), i.e., 0.97 % over the testing phase, among others models at BB. At the cross-station (DB), the performance of the XGBoost model was decreased by 2.74 % (R2) against random forests (RF). The mean absolute error (MAE) observed 40 % (XGBoost) and 47 % (RF) less than artificial neural network (ANN). The XGBoost model performance declined by 3.44 % (R2) over testing (DB), which is minor among validated models. At the cross-station (BB), the XGBoost model showed the least decrement in terms of R2, i.e., 7.99 % against the ANN (8.31 %), RF (10.26 %), and support vector machine (SVM, 36.19 %).

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011–2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.

An enhanced hybrid artificial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is significant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from five meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA), and Dragonfly Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.

Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the development of many attributes of the ecosystem in controlled quantity. In the present paper, an extreme gradient model (XGBoost) is proposed for Mn prediction. A compressive statistical analysis reveals the stochastics behaviour of the data prior to the prediction investigation. The main goal is to determine the Mn predictability of XGBoost algorithm with influencing factors such as D2EHPA (M), Time (min), H2SO4 (M), NaCl (g/L), and EDTA (mM). The PCA biplot signifies the importance of the predictors. The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF). The order of the applied models' performance are XGBoost > RF > SVM > MLR as per their R2 and RMSE metrics over testing phase i.e. 20.88, 0.75, 0.61, 0.40, and 2.23, 3.01, 3.51, 6.38, respectively. Moreover, the Taylor diagram and Radar chart have drown to emphasize the XGBoost model efficiency, stability, and reliability. In respect of XGBoost model prediction, ‘Time’ predictor outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.

The presence of various forms of heavy metals (HMs) (e.g., Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg, Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and agricultural runoff. HM removal in nature is highly stochastic, nonlinear, nonstationary, and redundant. Over the last two decades, the implementation of artificial intelligence (AI) models for HM removal has been massively conducted. The divergence in the selection of predictors, target variables, the optimization, normalization of the algorithm, function, and architecture of AI models are time-consuming processes, which limit the optimal use of such models for HM removal simulation. The selection of sustainable, cost-efficient, and user-friendly treatment techniques that have minimal reverse impact on the ecosystem is immensely challenging. The focus of the established researches is to find an optimal AI models for specific removal techniques. Predictors and target variables can be sorted using several techniques, and the selection of algorithm, function, and architecture based on individual treatment techniques have been coherently ordered and argued. In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly. The performance matrices are also discussed in accordance with the behavior of each model. Moreover, multiple perspectives that can enlighten interested multi-domain scientists and scholars, such as AI model developers, data scientists, wastewater treatment researchers, and environmental policymakers, on the actual status of the models’ progression are summarized. A comprehensive gap and assessments are also conducted to provide an insightful vision on this topic. Finally, several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.

With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549,627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.

Numerous researchers have expressed concern over the emerging water scarcity issues around the globe. Economic water scarcity is severe in the developing countries; thus, the use of inexpensive wastewater treatment strategies can help minimize this issue. An abundant amount of laundry wastewater (LWW) is generated daily and various wastewater treatment researches have been performed to achieve suitable techniques. This study addressed this issue by considering the economic perspective of the treatment technique through the selection of easily available materials. The proposed technique is a combination of locally available absorbent materials such as sand, biochar, and teff straw in a media. Biochar was prepared from eucalyptus wood, teff straw was derived from teff stem, and sand was obtained from indigenous crushed stones. In this study, the range of laundry wastewater flow rate was calculated as 6.23–17.58 m3/day; also studied were the efficiency of the media in terms of the removal percentage of contamination and the flux rate. The performances of biochar and teff straw were assessed based on the operation parameters and the percentage removal efficiency at different flux rates; the assessment showed 0.4 L/min flux rate to exhibit the maximum removal efficiency. Chemical oxygen demand, biological oxygen demand, and total alkalinity removal rate varied from 79% to ≥83%; total solids and total suspended solids showed 92% to ≥99% removal efficiency, while dissolved oxygen, total dissolved solids, pH, and electrical conductivity showed 22% to ≥62% removal efficiency. The optimum range of pH was evaluated between 5.8–7.1. The statistical analysis for finding the correlated matrix of laundry wastewater parameters showed the following correlations: COD (r = −0.84), TS (r = −0.83), and BOD (r = −0.81), while DO exhibited highest negative correlation. This study demonstrated the prospective of LWW treatment using inexpensive materials. The proposed treatment process involved low-cost materials and exhibited efficiency in the removal of contaminants; its operation is simple and can be reproduced in different scenarios.

It is well known that cotton mills consume large volume of water for various processes such assizing, desizing, scouring, bleaching, mercerization, dyeing, printing, finishing and ultimately washing.Contaminated air, soil, and water by effluents from the industries are associated with heavy disease burden(WHO, 2002) and this could be part of the reasons for the current shorter life expectancy in the country (WHO,2003) when compared to the developed nations. Some heavy metals contained in these effluents (either in freeform in the effluents or adsorbed in the suspended solids) from the industries have been found to becarcinogenic (Tamburlini et al., 2002) while other chemicals equally present are poisonous depending on thedose and exposure duration (Kupchella and Hyland, 1989). These chemicals are not only poisonous to humansbut also found toxic to aquatic life (WHO, 2002) and they may result in food contamination (Novick, 1999).There are sulphide and metal pollutant like fluoride, Arsenic, Molybdenum etc cause several harm effect to thelife directly on indirectly.Bhilwara has 4000 Textile manufacturing units which production exports to 70 countries specially inEurope, South Africa and North American. It has highest number of register private motor vehicle (4 wheeler) inAsia. This is second largest producer of polyester fiber in India, third largest producer of Salt i.e 1/10thproduction of country and only center in country for producing insulation bricks. This project is characterisedof textile industries’ effluents in Bhilwara and its Fluoride pollutant separation by Microbes.