Flood Prediction Using Machine Learning Models: A Case Study of Kebbi State Nigeria

Abstract

Machine Learning (ML) models for flood prediction can be beneficial for flood alerts and flood reduction or prevention. To that end, machine-learning (ML) techniques have gained popularity due to their low computational requirements and reliance mostly on observational data. This study aimed to create a machine learning model that can predict floods in Kebbi state based on historical rainfall dataset of thirty-three years (33), so that it can be used in other Nigerian states with high flood risk. In this article, the Accuracy, Recall, and Receiver Operating Characteristics (ROC) scores of three machine learning algorithms, namely Decision Tree, Logistic Regression, and Support Vector Classification (SVR), were evaluated and compared. Logistic Regression, when compared with the other two algorithms, gives more accurate results and provides high performance accuracy and recall. In addition, the Decision Tree outperformed the Support Vector Classifier. Decision Tree performed reasonably well due to its above-average accuracy and below-average recall scores. We discovered that Support Vector Classification performed poorly with a small size of dataset, with a recall score of 0, below average accuracy score and a distinctly average roc score.