5 hours flood prediction modeling using improved NNARX structure: case study Kuala Lumpur

Abstract

Flood is one of natural disaster that has becomes major threat around the world. Flood disaster may damages people's life and property. Therefore, an accurate flood water level prediction is very important in flood modelling because it can give ample time to residents nearby flood location for evacuation purposes. However, due to the dynamics of flood water level itself is highly nonlinear, Artificial Neural Network (ANN) technique is a good modelling option because ANN was widely used to solve nonlinear problems. NNARX is one type of ANN model. Therefore, this paper proposed flood prediction modelling to overcome the nonlinearity problem and come out with advanced neural network technique for the prediction of flood water level 5 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia upon special request. Results showed that the Improved NARX model successfully predicted the flood water level 5 hours ahead of time and significant improvement can be observed from the original NNARX model.