Rainfall Prediction Using Fuzzy Neural Network with Genetically Enhanced Weight Initialization

  • V. S. Felix EnigoEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


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.


Neural 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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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSSN College of EngineeringChennaiIndia

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