TEMPERATURE PREDICTION FROM REGIONAL SPECTRAL MODEL BY NEUROFUZZY GMDH

New businesses concerned with weather forecast have been emerged recently where the specific climate depending on geographical location and time of season is to be predicted. A neural GMDH (Group Method of Data Handling) family of modeling algorithm emulates the self–organizing activity of the central nervous system, and discovers the structure (functional form) of empirical models that include many input variables. A GMDH model called neurofuzzy (NF-) GMDH, whose partial descriptions (basic building blocks) are represented by the Radial Basis Functions (RBF) network, is applied to temperature forecast from the numerical weather prediction data of the Regional spectral model distributed by the Japan Meteorological Agency and compared with conventional RBF network. It is shown that the hierarchical GMDH type network outperforms the conventional RBF networks. Keyword:Neural network,Intelligent information processing,Weather Prediction 1 Introduction The weather forecasts influence the every day decisions of many people. For example, fishermen and ocean voyagers need precise and timely information to avoid storms and save fuel. Farmers depend on the forecasts to know when to plant and harvest their crops, when to apply chemicals to these crops. Aviators, in order to travel safely, need to know detailed weather along their route. Therefore new businesses concerned with weather forecast have been emerged recently where the specific climate depending on geographical location and time of season is to be predicted. Mathematical models of the multivariable system require a large number of parameters. Group Method of Data Handling (GMDH) [1] is one of the methods of identifying nonlinear systems with many input variables. Its mathematical model is represented by a hierarchical network of the partial descriptions (i.e., the basic building blocks) and discovers the structure of empirical models as well as performing the task of fitting model coefficients to bases of observational data. The GMDH family of modelling algorithms is classified into two categories by Ivakhnenko, that is, (1)the perceptron combination type (we briefly call ‘the perceptron type’) and (2)the network type. In the perceptron type GMDH algorithm, we merely take all of the independent variables one or two at a time and construct the partial descriptions. Several partial descriptions are selected by evaluating the mean squared error (MSE) with the checking data set. The outputs of selected partial descriptions are treated as the input in the next layer. These steps are repeated until a termination criterion is satisfied. The Adaptive Learning Networks (ALN) [2] developed in U.S.A. in the early 1970th are regarded as the network type GMDH in which the successive selections of partial descriptions of the perceptron type are not necessary, and this distinguishes the network type from the perceptron type. Recently new GMDH models, whose partial descriptions are represented by the Radial Basis Functions (RBF) networks or fuzzy models, have been developed [3]. We call them the neurofuzzy (NF-) GMDH. The RBF network is a technique for interpolating data in multidimensional spaces. The networks have the architecture that uses a single internal layer of locally tuned processing units and are called ‘localized receptive fields’ [4]. Brown and Harris demonstrated in their book entitled ‘Neurofuzzy adaptive modelling and control’ [5] that there exists an invertible relationship between fuzzy logic systems and RBF networks, with each inheriting the properties of the other. In this paper we applied these neurofuzzy GMDH model to temperature prediction by using the numerical weather prediction data distributed from the Regional Spectral Model of the Japan Meteorological