Flood prediction modeling using improved MLPNN structure: Case study Kuala Lumpur

Flood water level prediction has become subject matter around the world because it can cause damaging threat to human life and property. Therefore an accurate flood water level prediction is vital in order to alert residents nearby flood location of incoming flood events. However, since flood water level fluctuates highly nonlinear, it is a very difficult task to predict flood water level accurately. Hence, as nonlinear model and well known as a very effective solution for handling nonlinear problems, ANN was chosen in this study. This paper proposed a 1 hour ahead flood water level prediction modeling using Multilayer Perceptron Neural Network. Results shows significant improvement from the original MLPNN model when the improved model is introduced.