Machine Learning–Based Disaster Prediction Systems for Floods, Landslides, and Cyclones

<jats:p>Natural disasters such as floods, landslides, and cyclones pose significant challenges to communities worldwide, necessitating the development of advanced prediction systems. Machine learning (ML) has emerged as a transformative tool in disaster forecasting, offering enhanced predictive capabilities over traditional methods. This chapter explores the integration of machine learning models in disaster prediction, focusing on supervised and unsupervised learning approaches, deep learning techniques, and hybrid models. Emphasizing the role of data fusion, model optimization, and transfer learning, the chapter discusses their application in real-time, multi-disaster prediction systems. Special attention is given to low-data regions where pretrained models and transfer learning enable effective disaster forecasting despite limited local data. While ML-based systems show great promise, challenges such as model interpretability, overfitting, and computational demands remain significant barriers. By addressing these obstacles, the potential for machine learning to revolutionize disaster prediction and improve risk management strategies is immense. The chapter concludes by highlighting future research directions, including the integration of real-time data and the role of AI in building resilient, adaptive disaster management systems.</jats:p>