Application Research On Generalization Capability Of The Artificial Neural Network And Rainfall Forecast

Artificial Neural Network (ANN) is a nonlinear system that simulates theinformation processing method of the human brain, with strong ability to handlenonlinear problems, and adapts to the modeling for such problems as with complexinformation, dark background knowledge, or indefinite inference rules. With theapplication study of the NN on atmospheric science has been developed deeply, asignificant problem, i.e. the generalization capability of the ANN has been found inapplication of the ANN to the practical weather forecast operation. It is not onlyconcerned to the further application in the practical weather forecast operation, butalso a key technical problem unresolved in the application theoretic research of ANN.The application and theoretic research of ANN indicates that the generalizationcapability of the NN is closely related to the network structure, parameter and thesample quality. However, it is very difficult to decide the suitable network structure,to optimize the network parameter for better generalization capability of the ANNforecast model for a specific question. At present, the usual method to determine thestructure and network parameters is by means of repeating tests, and so, it oftenconduces to the overfitting problem, which affects the generalization ability of theNN model seriously.Because there is no objective quantitative method to settle the NN modelstructure theoretically in application of the ANN to the practical weather forecast indomestic and foreign countries, and the change of network model training number(i.e. the fitting precision of network model for the training samples) seriously affectsthe generalization capability, therefore, how to determine objectively the mostappropriate network structure to improve the generalization capability of the NNmodel is not only a matter of researched deeply in ANN modeling, but also a keytechnology to be resolved most urgently in the ANN application to the actualweather forecast operation presently.In view of the question that how to settle the network structure and to optimizethe network parameter, a new method is proposed in this article to determine the NN short-term weather prediction model objectively: the Genetic Algorithm (GA) wasused to optimize the connection weight and structure of the neural network, the bestindividual was retained in the genetic evolution process. Taking the Guangxiregional short-term rainfall prediction NN model and the intensity of west-forwardtyphoon short-term prediction NN model over South China Sea as examples to studyin this article, the main conclusions are as follows:(1) Optimizing the connection weight and network structure of NN with GA,and reserving the optimum individual in the evolution computation process is amethod which is able to solve the problems of the randomicity of initial weightvalues of the NN, and the objectivity in the determination of the NN structure, whichfrequently brings about oscillations in network training, thus leading to the localsolution. The practical calculation of short-term rainfall prediction ANN modelshows that the new approach avoids the difficulty of determination of the NNstructure by experience.(2) Making use of GA to determine the neural network structure, optimizing theconnection weight of neural network, so as to get the optimal neural networkstructure. The results show that the generalization capability of the GANN model ismuch better than the common NN model.With the problem that the quality of the training samples affects thegeneralization capability of the ANN model in establishment of short-term weatherforecast model, the effect of the learning matrix in NN forecast model with themulti-collinearity on the generalization capability is researched further. A new way isproposed, by using the principal components analysis (PAC) to construct the NNlearning matrix, so as to avoid the multi-collinearity and to enhance the quality of thetraining sample for the purpose of improving the generalization capability. TakingGuangxi regional short-term rainfall forecast NN model as example, the resultsuggests that in the context of the same input knot number, whatever the network is,smaller or getting larger, there is few changes in simulation error for both the neuralnetwork models, which of one with multi-collinearity and other without, the meansimulation errors for both of the two types model are very close to each other, but the generalization capability of the neural network with multi-collinearity is obvioussuperior than that without multi-collinearity. Further more, analyses of thegeneralization capability for the two types of models in different training times from5000 to 20000 indicates that the multi-collinearity have the remarkable effect ondecrease the forecast precision to the neural network forecast model.