Using Artificial Intelligence Methods for Efficient Water Resources Management

Abstract

Climate change, population growth, and urbanization pose significant challenges to water resources management. Traditional methods often fail to capture the complexity and nonlinearity of hydrological and climatic processes, necessitating the adoption of innovative approaches. Artificial Intelligence (AI) represents an effective tool for monitoring and forecasting water resources. This article discusses the application of AI methods-including machine learning, deep neural networks, and computer vision—for accurate prediction of water quantity and quality.

The study demonstrates that AI technologies enable the integration of data from diverse sources (meteorological, hydrological, satellite, and IoT sensors), enhancing the reliability of forecasts. Special attention is given to assessing changes in water levels, seasonal variability of precipitation, flood and drought risk evaluation, as well as water quality control and timely identification of pollution sources.

The results indicate that the integration of AI into water resources management not only increases the accuracy and efficiency of decision-making but also supports the implementation of sustainable development strategies. This creates a foundation for fair allocation of water resources and the protection of ecological safety on a global scale.