Rivers are a part of ecosystems, are increasingly vulnerable to the cumulative impacts of human activity, including urban sprawl, industrial discharge, and agricultural intensification. Addressing the complexity of these evolving threats requires some intelligent, data-driven approaches that move beyond conventional monitoring. This study presents NeuroAquaMorph, a comprehensive AI-powered framework designed to model, simulate, and forecast the morphological and physico-chemical evolution of aquatic systems under anthropogenic stress. By integrating diverse datasets ranging from water quality metrics and climatic variables to land use patterns and population density,the framework employs a hybrid suite of machine learning modelsincluding Random Forest, Support Vector Regression, and engineered LSTM-GRU simulations. The system is further enhanced through explainability techniques such as SHAP and permutation importance, ensuring transparency and trust in predictions. Detailed spatial and temporal analyses illuminate critical changes in river parameters like sediment load, microbial contamination, nutrient influx, and channel geometry. Interactive visualizations and scenario-based simulations offer actionable insights for sustainable river basin management and policy-making. With its modular architecture, interpretability, and scalability, NeuroAquaMorph represents a significant step toward intelligent environmental governance. The research concludes by outlining pathways for real-time integration, deep learning enhancement, and cross-regional application, aiming to support long-term ecological resilience and informed decision-making
Sharma et al. (2025) studied this question.