Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management

Abstract

The hydrological cycle is an important process that controls how and where water is distributed on Earth. It includes processes including transpiration, evaporation, condensation, precipitation, runoff, and infiltration. However, there are obstacles to understanding and modelling the hydrological cycle, such as a lack of data, ambiguity, fluctuation, and the impact of human activity on the natural balance. Techniques for accurate modelling are essential for managing water resources and risk reduction. With potential uses in rainfall forecasting, streamflow forecasting, and flood modelling, machine learning and artificial intelligence (AI) are effective tools for hydrological modelling. Case studies and real-world examples show how solutions to problems like data quality, interpretability, and scalability may be applied in real-world situations. Discussions of future directions and challenges emphasise new developments and areas that need more investigation and cooperation.