A study on flood forecasting at Sungai Lembing using artificial neural network - Siti Noor Alina, Mohd Zulkaply (2014)
Industrial countries which are rapidly developing had to faced environmental disaster. Flood occurs due to the excessive rainfall in the river catchment. The effects from flood are damage of properties and loss of life. Flood forecasting is necessities which will help in reduce the effects of flood and help better management planning of flood events. Statistical method such as Auto Regressive Moving Average (ARMA) is commonly used, but it is only a rough estimation of the flow. There is an alternative computing model that has been successfully tested in flood forecasting studies called Artificial Neural Network (ANN). It helps to produce an accurate forecasting result. The study is conducted to make accurate prediction of the flood event using Artificial Neural Network(ANN). The objective of the study is also to gain more understanding about Artificial Neural Network in data forecasting. Besides that, the objective of the study is to issue the flood warning. In this study, three iterations were conducted which is 1000, 2000 and 5000 iterations with six datasets of network model. The performance of training and validation data were evaluated using Nash Sutcliffe(NSC), correlation coefficient(R2), and Root Mean Square Error(RMSE). Error distribution graph are presented to show the accuracy and reliability of the forecasting models. The results showed that ANN able to provide accurate forecasting using sample historical datasets.