The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

(1) * Amrul Faruq Mail (University of Muhammadiyah Malang, Indonesia)
(2) Shahrum Shah Abdullah Mail (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)
(3) Aminaton Marto Mail (Department of Environmental Engineering and Green Technology, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)
(4) Mohd Anuar Abu Bakar Mail (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)
(5) Shamsul Faisal Mohd Hussein Mail (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)
(6) Che Munira Che Razali Mail (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)
*corresponding author

Abstract


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.


Keywords


Floods; Forecasting; Radial basis function; NARX; Artificial neural networks

   

DOI

https://doi.org/10.26555/ijain.v5i1.280
      

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