Flood disaster prediction model based on artificial neural network : a case study of Kuala Kangsar Perak.

• Main Authors: Shahrir, Nurul Syarafina, Ahmad, Norulhusna, Ahmad, Robiah, Dziyauddin, Rudzidatul Akmam
• Format: Conference or Workshop Item
• Published: ASIAN NETWORK ON CLIMATE SCIENCE AND TECHNOLOGY 2016
• Subjects: G70.212-70.215 Geographic information system
• Online Access: http://eprints.utm.my/id/eprint/66679/; http://mjiit.utm.my/dppc/2016/11/11/cfcce2016/
• Institution: Universiti Teknologi Malaysia

Natural flood disaster frequently happens in Malaysia especially during monsoon season and Kuala Kangsar, Perak is one of the cities with the frequent record of a natural flood disaster. Previous flood disaster faced by this city showed the failure in notify ing the citizen with sufficient time for preparation and evacuation. The authority in charge of the flood disaster in Kuala Kangsar depends on the real time monitoring from the hydrological sensor located at several stations along the main river. The real time information from hydrological sensor failed to provide early notification and warning to the public. Although many hydrological sensors available at the stations, only water level sensors and rainfall sensors are used by authority for flood monitoring. This study developed flood prediction model using artificial intelligent to predict the incoming flood in Kuala Kangsar area based on Artificial Neural Network (ANN). The flood prediction model is expected to predict the incoming flood disaster by using information from the variety of hydrological sensors. The study finds that the proposed ANN model based on Nonlinear Autoregressive Network with Exogenous Inputs (NARX) has better performance than other models with the correlation coefficient is equal to 0.98930. The NARX model of flood prediction developed in this study can be referred to future flood prediction model in Kuala Kangsar, Perak.