Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

Abstract

Flood is one of the most destructive phenomena all over the world. Because the flooding uncertainties and the urgency to prepare for disaster management, three specific technique approaches are compared in this study to predict the flood occurrence based on historical rainfall data. The study involved the rainfall data in Kemaman, Terengganu between 2017 and 2018 extracted from the official portal of the state of Terengganu. The dataset covers daily rainfall reading between January to December of the particular year in millimeter (mm) per day along with flood risks occurrence. This prediction experiment will be conducted using three variations algorithms, which are Decision Tree, Naive Bayes and Support Vector Machine. The comparison using three different algorithms was used to define the best algorithms that work with historical rainfall datasets to predict flood in terms of accuracy, precision, recall and F1-score. In the future, the prediction results are hoped to alert government authorities to make an early strategy to handle flood problems in Malaysia by analyzing the rainfall pattern.