Rainfall Prediction Using Fuzzy Neural Network with Genetically Enhanced Weight Initialization
In this paper, a hybrid approach of combining the techniques of artificial neural network along with fuzzy logic and genetic algorithm is used in prediction of rainfall classes. We have used genetic approach for initialization of weights in contrast to fixed weights or random weights for initialization of fuzzy neural networks. Fixed weights tend to get struck at local optimum by biasing the solution to a particular set of weights. Random initialization of weights increases the probability of obtaining global optimum solution in comparison to fixed weight approach. Our proposed genetic approach of weight initialization, rather than providing random weights, finds the optimal weights through genetic evolution for initialization of the fuzzy neural network. The proposed approach has been analyzed for rainfall classification system which predicts the class of one day ahead rainfall based on its intensity. By incorporating genetic evolved weights for fuzzy neural network, we were able to achieve a better accuracy than random weight approach.
KeywordsNeural network Fuzzy logic Genetic algorithm
The Meteorological data used in this study were obtained from Boundary Layer Meteorological Tower at Kalpakkam site operated and maintained by Radiological Safety Division, Indira Gandhi Centre for Atomic Research (IGCAR), India.
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