Flood prediction using ensemble machine learning model

Abstract

India experiences recurrent natural disasters in the form of floods, which result in substantial destruction of both human life and property. Accurately predicting the onset and progression of floods in real-time is crucial for minimizing their impact. This research paper focuses on a comparative study of various machine learning models for flood prediction in India. The evaluated models include K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision tree Classifier, Binary Logistic Regression, and Stacked Generalization (Stacking). We used a dataset of rainfall to train and test the models. Our results indicate that the stacked generalization model outperforms the other models, achieving an accuracy of 93.3% and Standard Deviation of 0.098. Our findings suggest that machine learning models can provide accurate and timely flood predictions, enabling disaster management authorities to take appropriate measures to minimize damage and save lives.