Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous input promoted with enhancing a forecast lead time to 12-hour. Both about the performance comparison has briefly been analyzed. The result verified the precision of error prediction of the presented flood forecasting model.

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Flood is considered chaotic, complex, volatile, and dynamics. Undoubtedly, its prediction is one of the most challenging tasks in time-series forecasting. Long short-term memory (LSTM) networks are a state of the art technique for time-series sequence learning. They are less commonly applied to the hydrological engineering area, especially for river water level time-series data for flood warning and forecasting systems. Yet it is inherently suitable for this subject. This paper examines an LSTM network for forecasting the river water level in Klang river basin, Malaysia. The river water level contains of single time series observed data, with time steps corresponding to hourly data and values corresponding to the water level or stage level in meters. In this study, prediction responses for river water level data using a trained recurrent neural network and update the network state function is applied. The result verified that the LSTM network with specified training set options is a promising alternative technique to the solution of flood modelling and forecasting problems. The performance indicates with the root mean square error, RMSE 0.20593 and coefficient of determination value, R 2 0.844 are closely accurate when updating the network state compared with the observed values.