Prediction of Flooding Due to Heavy Rainfall in India Using Machine Learning Algorithms: Providing Advanced Warning

Abstract

Floods are one of the deadliest disasters in the coastal areas of India. Consistently, flood, the most widely recognized catastrophe in India, has an enormous effect on the nation’s property and lives. Therefore, this article is focused on developing an effective flood-prediction system using machine learning (ML) algorithms that can help with preventing the loss of human lives and property. We will use k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs), and decision trees (DTs) to build our ML models. And to resolve the issue of oversampling and low accuracy, a stacking classifier will be used. For comparison among these models, we will use accuracy, f1-scores, recall, and precision. The results indicate that stacked models are best for predicting floods due to real-time rainfall in that area. It is noted that Andhra Pradesh achieves the highest accuracy of 97.91%, whereas Orissa achieves an accuracy of 92.36%, lowest among the eight coastal states.