Comparative Analysis of Various Machine Learning Techniques for Flood Prediction

A flood is a most destructive disaster that affects people, places, and lives. Due to the complication in data availability, flood prediction is always a challenging task. The conventional mode of disaster management relies on satellite images and radar outcomes. It takes enormous time for processing. Machine learning paved the way for a new perspective on this hydrological problem. Recent developments in Machine Learning (ML) and Information and Communication Technology (ICT) have led to a state-of-the-art implementation and prediction. The major objective of this work is to recognize the most accurate machine learning model to identify flood occurrence, by comparing Logistic regression, Decision Tree, Naive Bayes, and Support Vector Machines classifiers. Machine Learning strategies are evaluated using precision, recall, F1-score, RMSE, and accuracy metrics. All the strategies are applied to one-feature dataset, three-feature dataset and four-feature dataset. The quantitative evaluation demonstrates that decision tree algorithm is most suitable for flood prediction and it exponentially grows with respect to the number of features examined.