Flood Prediction Using Artificial Neural Networks: A Case Study in Temerloh, Pahang

Abstract

Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Besides predictive modeling, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods.