DOI : https://doi.org/10.18517/ijaseit.9.6.10239

Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting

Soo See Chai (1), Wei Keat Wong (2), Kok Luong Goh (3), Hui Hui Wang (4), Yin Chai Wang (5)
(1) Department of Software Engineering and Computing, Faculty of Computer Science and Information Technology, University Malaysia Sarawak (UNIMAS), Malaysia
(2) Department of Software Engineering and Computing, Faculty of Computer Science and Information Technology, University Malaysia Sarawak (UNIMAS), Malaysia
(3) School of Innovative Technology, International College of Advanced Technology Sarawak (i-CATS), Malaysia
(4) Department of Software Engineering and Computing, Faculty of Computer Science and Information Technology, University Malaysia Sarawak (UNIMAS), Malaysia
(5) Department of Software Engineering and Computing, Faculty of Computer Science and Information Technology, University Malaysia Sarawak (UNIMAS), Malaysia
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How to cite (IJASEIT) :
Chai, Soo See, et al. “Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 1921-6, doi:10.18517/ijaseit.9.6.10239.
Citation Format :
Mean daily rainfall of more than 30mm could result in flood hazard. Accurate prediction of rainfall intensity could help in forecasting of flash flood and help to save lives and properties. One of the common machine learning techniques in rainfall prediction is Radial Basis Function (RBF) neural network. Rainfall intensity is classified into four categories, i.e. light (<10mm), medium (11-30mm), heavy (31-50mm)  and very heavy (>50mm) in this study. The rainfall intensity categories is forecasted using the RBF network model utilizing the daily meteorology data for Kuching, Sarawak, Malaysia. The input vectors being considered for the RBF network model are minimum, maximum and mean temperature (°C), mean relative humidity (%), mean wind speed (m/s), mean sea level pressure (hPa) and mean precipitation (mm) for the year 2009 to 2013. The prime focus in this paper is to analyse the ramification of the training data size, number of hidden neurons, and different input variables (i.e. combination of meteorology data) in influencing the performance of the RBF network model. From this study, it could be concluded that, the factor that would influence the performance of the RBF model is only the input variables used, if and only if the network model is equipped with sufficient number of hidden neurons and trained with adequate number of training data. Another interesting observation from this study is that, the RBF network model produced consistent result throughout the testing using a specific hidden neuron number when the RBF network is retrained and tested.

Dou, X., J. Song, L. Wang, B. Tang, S. Xu, F. Kong, and X. Jiang, Flood risk assessment and mapping based on a modified multi- parameter flood hazard index model in the Guanzhong Urban Area, China. Stochastic Environmental Research and Risk Assessment, 2018. 32(4): p. 1131-1146.

Ouma, Y.O. and R. Tateishi, Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment. Water, 2014. 6(6): p. 1515-1545.

Archer, D.R. and H.J. Fowler, Characterising flash flood response to intense rainfall and impacts using historical information and gauged data in Britain. Journal of Flood Risk Management, 2018. 11(S1): p. S121-S133.

Seejata, K., A. Yodying, T. Wongthadam, N. Mahavik, and S. Tantanee, Assessment of flood hazard areas using Analytical Hierarchy Process over the Lower Yom Basin, Sukhothai Province. Procedia engineering, 2018. 212: p. 340-347.

Termeh, S.V.R., A. Kornejady, H.R. Pourghasemi, and S. Keesstra, Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 2018. 615: p. 438-451.

Lee, J., C.-G. Kim, J.E. Lee, N.W. Kim, and H. Kim, Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea. Water, 2018. 10(10): p. 1448.

Xingjian, S., Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. in Advances in neural information processing systems. 2015.

Wang, B., J. Lu, Z. Yan, H. Luo, T. Li, Y. Zheng, and G. Zhang Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting. arXiv e-prints, 2018.

Aggarwal, R. and R. Kumar, A Comprehensive Review of Numerical Weather Prediction Models. International Journal of Computer Applications, 2013. 74(18).

Bellier, J., I. Zin, S. Siblot, and G. Bontron. Probabilistic flood forecasting on the Rhone River: evaluation with ensemble and analogue-based precipitation forecasts. in E3S Web of Conferences. 2016. EDP Sciences.

Abbot, J. and J. Marohasy, Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia. Engineering and Mathematical Topics in Rainfall, 2018: p. 33.

Sittichok, K., A.G. Djibo, O. Seidou, H.M. Saley, H. Karambiri, and J. Paturel, Statistical seasonal rainfall and streamflow forecasting for the Sirba watershed, West Africa, using sea-surface temperatures. Hydrological Sciences Journal, 2016. 61(5): p. 805-815.

Tharun, V., R. Prakash, and S.R. Devi. Prediction of Rainfall Using Data Mining Techniques. in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). 2018. IEEE.

Meyer, H., M. Kí¼hnlein, T. Appelhans, and T. Nauss, Comparison of four machine learning algorithms for their applicability in satellite- based optical rainfall retrievals. Atmospheric research, 2016. 169: p. 424-433.

Cramer, S., M. Kampouridis, A.A. Freitas, and A.K. Alexandridis, An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 2017. 85: p. 169-181.

Chai, S.-S., W.K. Wong, and K.L. Goh, Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data. JCS, 2016. 12(4): p. 191- 200.

Broomhead, D.S., Multivariate functional interpolation and adaptive networks. Complex Systems, 1988. 2(3): p. 321-355.

Bakar, M.A.A., F.A.A. Aziz, S.F.M. Hussein, S.S. Abdullah, and F. Ahmad. Flood Water Level Modeling and Prediction Using Radial Basis Function Neural Network: Case Study Kedah. In Asian Simulation Conference. 2017. Springer.

Chang, F.-J., J.-M. Liang, and Y.-C. Chen, Flood forecasting using radial basis function neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2001. 31(4): p. 530-535.

Tezel, G. and M. Buyukyildiz, Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and Applied Climatology, 2016. 124(1): p. 69-80.

El Shafie, A.H., A. El-Shafie, A. Almukhtar, M. Taha, H.G. El Mazoghi, and A. Shehata, Radial basis function neural networks for reliably forecasting rainfall. Journal of water and climate change, 2012. 3(2): p. 125-138.

Vivekanandan, N., Prediction of Rainfall Using MLP and RBF Networks. International Journal of Advanced Networking and Applications, 2014. 5(4): p. 1974.

Biswas, S.K., L. Marbaniang, B. Purkayastha, M. Chakraborty, H.R. Singh, and M. Bordoloi, Rainfall forecasting by relevant attributes using artificial neural networks-a comparative study. International Journal of Big Data Intelligence, 2016. 3(2): p. 111-121.

Sa’adi, Z., S. Shahid, T. Ismail, E.-S. Chung, and X.-J. Wang, Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test. Meteorology and Atmospheric Physics, 2017.

Kim, T., W. Ko, and J. Kim, Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting. Applied Sciences, 2019. 9(1): p. 204.

Markopoulos, A.P., S. Georgiopoulos, and D.E. Manolakos, On the use of back propagation and radial basis function neural networks in surface roughness prediction. Journal of Industrial Engineering International, 2016. 12(3): p. 389-400.

Moghaddam, A.H., M.H. Moghaddam, and M. Esfandyari, Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 2016. 21(41): p. 89-93

Barati-Harooni, A., A. Najafi-Marghmaleki, and A.H. Mohammadi, A reliable radial basis function neural network model (RBF-NN) for the prediction of densities of ionic liquids. Journal of Molecular Liquids, 2017. 231: p. 462-473.

Wang, Y.-X. and M. Hebert. Learning to Learn: Model Regression Networks for Easy Small Sample Learning. 2016. Cham: Springer International Publishing.

Haque, M., A. Rahman, D. Hagare, and R. Chowdhury, A comparative assessment of variable selection methods in urban water demand forecasting. Water, 2018. 10(4): p. 419.

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