Abstract
This paper evaluates the performance of a rainfall forecasting model. In this paper Artificial Neural Network (ANN) and Fuzzy C-Means (FCM) clustering algorithm are combined and used to forecast short-term localized rainfall in tropical climate. State forecast (raining or not raining) and value forecast (rain intensity) are tested using a number of trained networks. Different types of ANN structured were trained with a combination of multilayer perceptron with back propagation network. Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient training algorithm are used in the network training. Each neurons uses linear, logistic sigmoid and hyperbolic tangent sigmoid as transfer function. Input parameter preliminary analysis, data cleaning and FCM clustering were used to prepare input data for the ANN forecast model. Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used as input parameters. The predicted rainfall forecast for 1 to 6 h ahead are compared and analyzed. 1 h ahead for state and value forecast yield high accuracy. Result shows that, the combined of FCM-ANN forecast model produces better accuracy compared to a basic ANN forecast model.
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References
Pradhan, B.: Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J. Spat. Hydrol. 9, 1–18 (2009)
Shahi, A.: An effective fuzzy C-mean and type-2 fuzzy. J. Theor. Appl. Inf. Technol. 5, 556–567 (2009)
Maqsood, I., Khan, M., Abraham, A.: An ensemble of neural networks for weather forecasting. Neural Comput. Appl. 13, 112–122 (2004)
Hu, M.J.C., Root, H.E.: An adaptive data processing system for weather forecasting. J. Appl. Meteorol. 3(5), 513–523 (1964)
Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13(8), 1413–1425 (2008)
Klent Gomez Abistado, C.N.A., Maravillas, E.A.: Weather forecasting using artificial neural network and bayesian network. J. Adv. Computat. Intell. Intell. Inf. 18(5), 812–817 (2014)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)
Bezdek, J.C.: FCM: the fuzzy C-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)
Lu, Y., Ma, T., Yin, C., Xie, X., Tian, W., Zhong, S.: Implementation of the fuzzy C-means clustering algorithm in meteorological data. Int. J. Database Theory Appl. 6(6), 1–18 (2013)
Vega-corona, A.: ANN and Fuzzy c-means applied to environmental pollution prediction, pp. 1–6 (2012)
Howard Demuth, M.B., Hagan, M.: Neural network toolbox user’s guide (2012)
Neural Networks Training Based on Differential Evolution Algorithm Compared with Other Architectures for Weather Forecasting34. J. Comput. Sci. Netw. Secur. 9(3), 92–99 (2009)
Cheng, B., Titterington, D.M.: Neural Networks: A Review from a Statistical Perspective. Stat. Sci. 9(1), 2–30 (1994)
Goss, D.F.E.: Forecasting with neural networks: an application using bankruptcy data. Inf. Manage. 24(3), 159–167 (1993)
Abraham, A., Philip, N.S., Joseph, K.B.: Will we have a wet summer ? In: Soft Computing Models for Long-term Rainfall Forecasting (1992)
Charalambous, C.: Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proc. G Circuits, Devices Syst. 139(3), 301 (1992)
Foresee, F.D., Hagan, M.T.: Gauss-Newton approximation to Bayesian learning. In: Proceedings of International Conference on Neural Networks (ICNN 1997) (1997)
Pellakuri, V., Rajeswara Rao, D., Lakshmi Prasanna, P., Santhi, M.V.B.T.: A conceptual framework for approaching predictive modeling using multivariate regression analysis vs artificial neural network. J. Theor. Appl. Inf. Technol. 77(2), 287–290 (2015)
Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Lecture Notes in Mathematics, pp. 105–116. Springer, Berlin (1978)
MacKay, D.J.C.: A practical bayesian framework for back propagation networks. Neural Comput. 4(3), 448–472 (1992)
MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)
Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Science 49(CS-94–125), 64 (1994)
Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)
Hauke, J., Kossowski, T.: Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011)
Yuan, H.L., Gao, X.G., Mullen, S.L., Sorooshian, S., Du, J., Juang, H.M.H.: Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network. Weather Forecast. 22(6), 1287–1303 (2007)
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Mohd-Safar, N.Z., Ndzi, D., Sanders, D., Noor, H.M., Kamarudin, L.M. (2018). Integration of Fuzzy C-Means and Artificial Neural Network for Short-Term Localized Rainfall Forecast in Tropical Climate. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_38
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DOI: https://doi.org/10.1007/978-3-319-56991-8_38
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