Spur dikes are hydraulic structures widely used to divert the flow from riverbanks to the centerline. The reason for deviating the flow is to protect the bed and prevent erosion around banks. But the spur stability becomes at risk due to the flow vortex around it. Hence, it is vital to investigate the flow patterns around these spurs, either by physical means or numerical analysis. Since Physical Modeling requires a lot of effort, it is essential to conduct further study with a new approach and a technique with less hassleusing Computational Fluid Dynamics software named FLOW-3D to simulate complex hydrodynamics problems. In this paper, a Renormalized Group (RNG) k-ε turbulence model was employed to address the flow pattern around spur dikes. The model was validated using the experimental data results present in the open literature. Grid sensitivity analysis was also conducted, which provided evidence that a finer mesh using the nested mesh technique produced better results than a coarse mesh. Statistical tests, the coefficient of regression (R^2), root mean square deviation (RMSD), and mean absolute error (MAE) are utilized to compare the data on the transverse velocities in different sections to check the accuracy of the model with the observed data. For a 4 cm spur in a laboratory flume, the R^2, RMSD, and MAE were obtained as 0.97, 0.011, and 0.004, respectively, which shows the good agreement of the model results with the observed data, with minimal discrepancies. Therefore, it is recommended to be utilized for similar studies in the future and is applicable to field conditions.

Water quality assessment is crucial for environmental management and public health, particularly in regions like Kerala, India, where rivers play a vital role in the ecosystem and human activities. This study investigates the water quality of 44 rivers in Kerala, India, using machine learning techniques to classify water quality based on specific parameters. The data, sourced from the Kerala State Pollution Control Board's Water and Air Quality Directory 2023, include measurements of pH, biochemical oxygen demand (BOD), dissolved oxygen (DO), electrical conductivity (EC), and total coliform concentration. These parameters were used to categorize water into five distinct classes: Class A (drinking water source without conventional treatment but after disinfection), Class B (outdoor bathing), Class C (drinking water source after conventional treatment and disinfection), Class D (propagation of wildlife and fisheries), and Class E (irrigation, industrial cooling, controlled waste disposal). Three machine learning models were employed for classification: support vector machine (SVM), k-nearest neighbors (KNN), and decision tree (DT). The dataset was split into training and testing sets to evaluate the models' performance. Among the models, the SVM achieved the highest accuracy, classifying water quality with an accuracy of 92.83%. The results demonstrate the effectiveness of machine learning in assessing and classifying river water quality, providing a valuable tool for environmental monitoring and management. This study highlights the potential of advanced data analysis techniques to support public health and environmental conservation efforts by accurately identifying water quality categories based on standardized criteria.

Groundwater in open-pit mines can significantly influence mining operations, affecting both safety and productivity. A thorough understanding of groundwater conditions and their impact on slope stability is essential for optimizing mine design and ensuring operational efficiency. This study investigates the effects of groundwater on the slope stability of the western pit in the Sangan iron ore mine through detailed numerical modeling. The graphical model revealed that in the tenth year of mining operations, the safety factors in the lower benches fell below 1.3, indicating potential instability based on the Mohr–Coulomb failure criterion. This instability poses a significant risk to the structural integrity of the mine. Further analysis demonstrated that reducing pore pressure could substantially increase the factor of safety, thereby mitigating the risk of slope failure. To address this critical issue, a comprehensive dewatering program was meticulously designed, modeled, and implemented to effectively reduce or eliminate pore pressure, ultimately enhancing pit slope stability. The validity of the numerical model was rigorously confirmed through subsequent analysis, demonstrating its reliability as a predictive tool for groundwater flow and slope stability assessment in similar mining environments. This study underscores the crucial importance of incorporating groundwater management into the design and operational planning of open-pit mines to ensure long-term stability and safety.

Excessive groundwater extraction for agriculture, combined with climate change, is depleting groundwater reserves and degrading their quality. This threatens both groundwater-dependent economies and ecosystems. In the Duero River basin in Spain, four groundwater bodies are in poor quantitative condition, and eighteen have a poor chemical status.

Within the European project STARS4Water (Supporting Stakeholders for Adaptive, Resilient, and Sustainable Water Management), a management system has been developed using the Groundwater Resource Availability Index (GWAI), reference levels, and management guidelines. This system aims to be universally applicable and flexible enough to adapt to various hydrological, climatic, and management contexts. The GWAI is designed to be calculated easily using data from standard monitoring programs, and its results should be easily interpretable by various stakeholders.

The GWAI calculation involves defining a ‘calculation period’ and determining the normalized slope of piezometric-level changes during that period. This is carried out iteratively, starting from the beginning of the historical piezometric data series, with the calculation window moving forward by one year in each iteration. This process reveals both the evolution of the index and its individual values. Management recommendations are based on the index's evolution over time, where negative GWAI values indicate resource depletion and positive values indicate increasing resource availability. These calculations were automated in a user-friendly spreadsheet program.

The GWAI was applied across the Duero River basin and in more detail to the Los Arenales–Tierra de Medina–La Moraña groundwater body, these areas being severely affected by resource quantity and chemical status issues.

Sindh Province has experienced significant alterations in temperature and precipitation, resulting in heightened occurrences of extreme weather like heatwaves, droughts, and floods, exacerbating environmental challenges in frequency and intensity. This study utilized the multi-model ensemble of the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze historical and future climatic projections in Sindh Province, Pakistan. This study aim to develop high-resolution, region-specific estimates that capture the subtle implications of various climatic scenarios and Shared Socioeconomic Pathways (SSPs) using modern climate models and downscaling approaches. These changes in climate have amplified the occurrence and intensity of various environmental challenges. The findings are categorized into two time periods: the near future (2030-2060) and the far future (2060-2100) under the Shared Socioeconomic Pathways (SSPs). This study aims to increase scientific knowledge of how Sindh Province is being affected and will be affected by climate change. The results show that the temperature will continue to increase in the future in Sindh. However, models project uncertain precipitation patterns, including an increased frequency of extreme events (floods, heatwaves, and droughts) in this region. The insights will help policymakers and water managers in preparing sustainable and climate-resilient water management strategies in this region.

The zero-inertia/diffusive-wave hydrodynamic model is often numerically solved as a nonlinear parabolic/diffusion differential problem, similar to the heat equation. In this approach, the function of the sought solution usually represents the elevation of the water surface of the flow. Such numerical solutions have been implemented using the finite difference, finite volume, or finite element method and are applied in the estimation of river flow, watershed runoff, or flood inundation, for example.

In this study, the two-dimensional zero-inertia/diffusive-wave hydrodynamic model was solved as a nonlinear advection differential problem. The function of the sought solution is the thickness (sometimes called flow height) of the water flow. An implicit finite difference scheme was applied for the time domain. In order to handle irregular geometries, the Galerkin finite element method was employed. To enhance the stability of the numerical solution, stabilization terms were added to the numerical scheme, namely, the streamline-upwind Petrov-Galerkin (SUPG) term and the spurious-oscillations-at-layers diminishing (SOLD) term. Different types of boundary conditions at the outflow boundaries were imposed.

When simple tests were carried out, the results were in good agreement with the analytical or some existing numerical solutions. While more complicated tests were conducted, the results were still realistic, although with defects.

The presented approach can be used for numerical solutions with longer time domains. Further investigations are needed to improve the numerical solution in the frame of this approach. Future studies can help in choosing better numerical schemes for the time domain and other stabilization terms in the finite element method.

Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations, Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt–Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.

This study investigates the enhancement of the photoelectrochemical degradation of dyes in aqueous solutions using TiO2 nanorod photoanodes modified with cerium oxide (CeO2) via electrodeposition techniques. Two different polymerization methods, constant potential and pulsed potential, were employed to deposit CeO2 on the TiO2 nanorods. The pulsed potential method was found to significantly outperform the constant potential method, demonstrating superior photocurrent generation. The modified photoanodes were tested for their ability to degrade methylene blue and methyl orange dyes under visible light irradiation. The TiO2 nanorod photoanodes with CeO2 deposited using the pulsed potential method exhibited the highest efficiency in dye degradation, which is attributed to the optimized cerium oxide deposition and enhanced charge transfer properties achieved through the pulsed electrodeposition technique. The improved performance of the pulsed-potential-modified photoanodes highlights their potential for application in environmental remediation, particularly for the treatment of dye-contaminated water. This study not only demonstrates the effectiveness of pulsed electrodeposition in enhancing the photocatalytic performance of TiO2 nanorod photoanodes but also provides valuable insights into the development of advanced photocatalytic materials for water purification. The findings underscore the benefits of using pulsed deposition techniques to achieve high-performance photoanodes for the efficient photoelectrochemical degradation of organic pollutants, offering a promising approach to address environmental challenges associated with dye pollution in water.

As global water resources face unprecedented challenges from population growth, climate change, and urbanization, innovative technologies are essential for sustainable water management. This study explores the application of Internet of Things (IoT) and Artificial Intelligence (AI) within Smart Water Management Systems (SWMS), highlighting their transformative potential in urban water networks. IoT-enabled devices offer continuous real-time monitoring of water parameters, providing a wealth of data that AI algorithms can analyse to optimize water distribution, detect leaks, and manage water quality. The implementation of Digital Twin technology allows for the simulation and analysis of various water management scenarios, enhancing decision-making processes and operational efficiency. This research presents case studies demonstrating the effectiveness of IoT and AI in predicting water demand patterns, identifying system failures, and improving overall water management resilience and sustainability. The integration of these technologies not only reduces operational costs but also enhances environmental protection, aligning with the goals of sustainable development and risk mitigation in water resource management. Our findings contribute to the ongoing discourse on smart water grids, showcasing how IoT and AI can be effectively integrated into traditional water management infrastructures. This study provides a comprehensive roadmap for future advancements in water technology, emphasizing the importance of innovative approaches in addressing the complexities of modern water management​.

Background and Aims: This study explores the application of artificial intelligence (AI) in irrigation management to optimize soil, water, and fertilizer consumption in agriculture. The growing world population and the subsequent increase in demand for agricultural products, coupled with climate change, have put increasing pressure on water resources. Therefore, efficient soil and water management practices are necessary. AI techniques such as machine learning, neural networks, and the Internet of Things (IoT) are being utilized to analyze various data sources including weather patterns, soil conditions, crop types, and water levels.

Methods: The working method in this research includes collecting information from the articles listed in the References Section and examining them in detail in order to identify the types of sub-branches of artificial intelligence used in studies related to climate change in different sectors of agriculture. More than 41,000 full titles in 130 reference databases were simultaneously reviewed. A total of 26 primary studies were selected to form the basis of this review.

Results: This study emphasizes how studies on water and soil management heavily rely on artificial intelligence (AI) techniques based on artificial neural networks (ANNs) and fuzzy logic. ANNs have shown great performance and are primarily utilized for machine learning-based solutions. Compared to other AI techniques or even well-known regression methods, these networks are frequently more effective. In the assessment of soil and water issues, ANN-based solutions have also been shown to be more effective than traditional equations, particularly in situations when limited data are available.

Conclusions: This enables farmers to make informed decisions in agricultural operations to safeguard and manage water and soil resources effectively. In conclusion, this study underscores the potential benefits of integrating AI technologies in irrigation management for sustainable agricultural practices in an increasingly water-scarce world.

Keywords: water and soil management, fuzzy logic, artificial neural networks, climate change, irrigation