Prediction and Visualization of the Disaster Risks in the Philippines Using Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN)

Abstract

This study is all about predicting and visualizing the disaster risks in the Philippines brought by tropical cyclones. According to UNICEF Philippines (2017), the Philippines is highly exposed to natural hazards because it lies along the Pacific Typhoon Belt compounded by uncontrolled settlement in hazard-prone areas, high poverty rate, and failure to implement building codes and construction standards. The number of casualties resulting from these incidents can be reduced if the possible occurrence of risk can be foreseen which can help the community have awareness and form recommendations ahead. The researchers of this study improved the results of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) by using a de-noising model of Discrete Wavelet Transform (DWT), in predicting the disaster risk levels of the Philippine provinces in terms of casualties, damaged houses, and damaged properties. Based on the result of the study, the best model for casualty, damaged houses, and damaged properties is the DWT-ARIMA-ANN model.