The Use of Machine Learning in Predicting and Preventing Natural Disasters: An Empirical Study

Abstract

This research focuses on various Machine Learning (ML) techniques for predicting natural disasters and suggest ways to prevent overwhelming shocks such as floods, storms. It is related to the urgent global issue of disasters increasing because of climate change. We need a system that improves our forecasting in real time. The study focuses on historical and forecast data including a sea surface temperature anomalies, rainfall anomalies and vegetation indices, and development of models for early warning and damage risk assessment.

Models like Linear Regression, K Nearest Neighbor (KNN), Random Forest and Long Short-Term Memory (LSTM) trick Random Forest the highly accurate and stable model among the various models which gives better precision and recall among the various metrics. Model parameters that characterise the environment, namely the meteorological variables play a more important role than the socio-economic parameters. The study also examines the social and technical implications of utilising ML-based systems for forecasting disasters especially in those emerging urban areas that have a high human and economic vulnerability The study’s outcomes suggest that the application of adaptive, ensemble ML model can help in better decision-making and loss reduction. It was suggested that the findings of this paper be included in the Internet of Things that is to come. Further, hybrid machine learning systems should be evolved and implemented for better accuracy. Besides, it will enable governments to create policy for disaster management which is sustainable globally.