Artificial neural networks for water level prediction based on z-score technique in Kelantan river

In Malaysia, flood can happens annually anytime of the year in multitude of ways. This study aimed to predict water level at Jeti Kastam station (S6) in Kelantan River using an Artificial Neural Networks (ANN) as a modelling tool and validate the accuracy of the model. The Z-Score technique is applied to previous rainfall and water level data to all 6 stations along Kelantan River in identified the significant stations before the successful data resulted will fed to ANN model network. The ANN model was formulated to simulate water level using feedforward algorithm. Readings from 6 stations from rainfall stations showed that S1, S2, S3 and S6 code station while S1 and S2 for water level station were significant value based on ZScore processing method. Total of 1095 data per station collected from January 2013 until December 2015 was used for training, validation and testing of the network model. Mean Square Error (MSE) and Regression analysis, R are calculated every node. The result showed that the 5 hidden nodes in hidden layer revealed that the regression, R for training, validation and testing were 0.9993, 0.9640 and 0.9989 respectively with MSE value was 2.14e-05. The result of prediction model has found to be suitable to predict flood model by training function feedforward optimization