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http://dx.doi.org/10.5370/KIEE.2014.63.4.526

A Study on Heavy Rainfall Guidance Realized with the Aid of Neuro-Fuzzy and SVR Algorithm Using AWS Data  

Kim, Hyun-Myung (Dept. of Electrical Engineering, The University of Suwon, Korea & Korea and National Institution of Meteorological Research)
Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
Kim, Yong-Hyuk (Dept. of Computer Science, Kwangwoon University)
Lee, Yong-Hee (Korea and National Institution of Meteorological Research)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.63, no.4, 2014 , pp. 526-533 More about this Journal
Abstract
In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.
Keywords
RBFNNs(Radial Basis Function Neural Networks); FCM(Fuzzy C-means Method); PSO(Particle Swarm Optimization); SVR(Support Vector Regression); AWS(Automatic Weather Station); ETS(Equitable Threat Score); undersample;
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