Modeling of flood water level prediction using improved RBFNN structure

Recently, the applications of Artificial Neural Network (ANN) in various hydrologic problems have becoming popular. This is due to ability of ANN models to estimate nonlinear functions and hence become important tools to solve diverse water resources problems. Particularly, ANN models have been used in hydrological fields such as river flow forecasting, rainfall-runoff estimation, flood prediction and water quality prediction. Therefore, this paper proposed flood water level prediction model using Radial Basis Function Neural Network (RBFNN) and Improved RBFNN structure that using the water level data from Kelang river which is located at Jambatan Petaling, Kuala Lumpur. The models were developed by processing offline data over time using neural network architecture. The methodologies and techniques of the two models were presented in this paper and comparison of the long term runoff time prediction results between them were also conducted. The prediction results of the Radial Basis Function Neural Network architecture indicate fair performance for the one hour ahead of time prediction. The performance indices results also concluded that the Improved RBFNN model was more reliable than that of the original RBFNN model.

[1]  Prabir Kumar Biswas,et al.  A scalable model for knowledge sharing based supervised learning using AdaBoost , 2015, 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR).

[2]  Wing W. Y. Ng,et al.  Empirical study on the architecture selection of RBFNN using L-GEM for multi-class problems , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[3]  Ruben Gonzalez Rubio,et al.  Artificial neural network simulator with integrated learning supervision , 1995, Canadian Journal of Electrical and Computer Engineering.

[4]  Pietro Burrascano,et al.  A norm selection criterion for the generalized delta rule , 1991, IEEE Trans. Neural Networks.

[5]  Abd. Manan Samad,et al.  Prediction of 4 hours ahead flood water level using improved ENN structure: Case study Kuala Lumpur , 2014, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014).

[6]  Gurpreet Singh,et al.  Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition , 2014, 2014 IEEE International Conference on Computational Intelligence and Computing Research.

[7]  Xun Gong,et al.  Modeling and robust backstepping sliding mode control with Adaptive RBFNN for a novel coaxial eight-rotor UAV , 2015, IEEE/CAA Journal of Automatica Sinica.

[8]  Ramli Adnan,et al.  Flood prediction using NARX neural network and EKF prediction technique: A comparative study , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[9]  Ching-Chih Tsai,et al.  Two DOF temperature control using RBFNN for stretch PET blow molding machines , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[10]  Ramli Adnan,et al.  Flood water level modeling and prediction using NARX neural network: Case study at Kelang river , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[11]  Jiang-Whai Dai,et al.  Using grey and RBFNN to predict the net asset value of single nation equity funds-a case study of Taiwan, US, and Japan , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[12]  Yan-Jun Liu,et al.  Training RBFNN with reglarized correntropy criterion and its application to foreign exchange rate forecasting , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[13]  Girish V. Lakhekar,et al.  A fuzzy neural approach for dynamic spectrum allocation in cognitive radio networks , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].