The Role of Machine Learning in Predicting Natural Disasters                                     

Claire Niyonzima K.

Faculty of Engineering Kampala International University Uganda

ABSTRACT

Natural disasters such as earthquakes, floods, and storms have a profound impact on human life and infrastructure. Despite advancements in early warning systems and disaster preparedness, predicting these events with high accuracy remains a significant challenge. Machine learning (ML) offers promising solutions for enhancing prediction accuracy and mitigating the adverse effects of natural disasters. This paper explores the role of ML in predicting various natural disasters by analyzing large datasets, extracting relevant features, and applying advanced algorithms. The study also examines the challenges associated with data quality, model transferability, and the nonstationary nature of natural disasters. Case studies demonstrate the practical applications of ML in real-world scenarios, highlighting its potential to revolutionize disaster prediction and risk management. Finally, the paper discusses future directions for research in ML, focusing on improving model robustness, feature engineering, and the integration of synthetic data to better predict natural disasters.

Keywords: Machine Learning, Natural Disaster Prediction, Earthquakes, Floods, Tsunamis.

CITE AS: Claire Niyonzima K. (2024). The Role of Machine Learning in Predicting Natural Disasters. RESEARCH INVENTION JOURNAL OF BIOLOGICAL AND APPLIED SCIENCES 3(2):20-23.