Role of Artificial Intelligence in Hydrological Modelling

Abstract

Hydrological modelling involves simulating the movement, distribution and behaviour of water within a watershed or catchment area using mathematical equations and computational tools. It uses simplified representations of realworld systems to understand, predict, and manage water resources, encompassing processes like precipitation, runoff, river discharge, evaporation, infiltration, flood forecasting and groundwater flow. These models are useful in water resource management, flood control, drought assessment, and climate change impact studies. These models are useful in predicting the quantity and quality of water in a watershed necessary for the effective management of water resources [Kambarbekov and Baimaganbetov, 2024]. There are many traditional hydrological models developed to apply on numerous watersheds to find out the impact of climate and soil properties on hydrology and water resources [Devia et al., 2015]. The best model is the one that gives close to reality results with the use of the least parameters and the complexity of the model [Devia et al., 2015]. These models are classified into the following types: