Enhanced Disaster Forecasting Through Machine Learning, Weather Data, and Remote Sensing

Natural disasters, driven by different physiological systems, bring a deadly danger to human life, structures, and surroundings. Their appearance, frequently unpredictable, is a result of the interaction of multiple natural events including earthquakes, cyclones, and floods. They originate from factors such as seismic events, climatological conditions, and anthropogenic activities which become a great source of further aggravation. The randomness and ferocity of natural catastrophes emphasize the long overdue implementation of better forecasting measures to oversee their impact. Conventional approaches to disaster forecasting have been confronted by insufficiently precise and prompt forecasts, jeopardizing the populations and leading their vulnerable life. Consequently, the research proposes an integration of historical Weather Parameters of Jharkhand, remote sensing, and machine learning approaches to enhance model accuracy and provide a novel approach. The model harnesses the efficiency of K-nearest neighbour's (KNN) to predict high-indexed precipitation, ARIMA time series for earthquake predictions, and loads the correlations between using rainfall for tsunami prediction. The model aims at providing timely and accurate predictions in this way, thereby enabling communities & authorities to enact timely measures and diminish disasters' impact on livelihoods.