Hydrometeorological events, such as floods and droughts, pose significant challenges to societies worldwide, causing loss of life and economic damage. Traditional methods of predicting such events often rely on statistical and physical models limited by their assumptions, uncertainties, and computational requirements. Machine learning (ML) techniques, with their ability to extract knowledge and insights from data, have shown great potential for improving the accuracy and lead time of hydrometeorological event prediction. This chapter reviews the use of ML for predicting hydrometeorological events, focusing on flood and drought events. The chapter provides an overview of the application of ML techniques or algorithms for predicting hydrometeorological events. The chapter discusses data type, collection, and analysis for ML applications for predicting hydrometeorological events. The chapter presents case studies from different regions and highlights the benefits and challenges of ML-based approaches. Finally, the chapter identifies future research directions.
Citation: Msigwa, A. & Makinde, A. S. (2024). Application of Machine Learning Techniques in Hydrometeorological Event Prediction. In C. Maftei, R. Muntean, & A. Vaseashta (Eds.), Modeling and Monitoring Extreme Hydrometeorological Events (pp. 135-161). IGI Global.
