Devendra KumarKota Spandana2019-01-292019-01-292018-06http://krishikosh.egranth.ac.in/handle/1/5810092754In the present study, artificial neural network technique has been employed to predict monthly rainfall for Medak, Khammam and Warangal stations of Central Telangana, India. Eighty five years of rainfall data (January, 1901 to December, 1985) were used for training of models and twenty eight years of rainfall data (January, 1986 to December, 2014) were used for testing of models. Gamma test, autocorrelation function and cross correlation function were used for selection of appropriate input variables. The ANN models were trained using multilayer perceptron with two learning rules i.e. Levenberg-Marquardt and Delta-bar-delta and two transfer functions viz. sigmoid axon and Tanh axon. The performance of the models was evaluated qualitatively by visual observation and quantitatively by using different performance indices viz. Root Mean Square Error, Correlation Coefficient, Coefficient of Efficiency, Percent Bias and Integral Square Error. It was observed that the better results of monthly rainfall prediction of developed models were observed when the rainfall data of adjoining stations were used as the input variable as compared the lagged rainfall of the same station. The higher value of Correlation Coefficient and Coefficient of Efficiency and lower value of Integral Square Error, Percent Bias and Root Mean Square Error suggest that the M-8 model, K-7 model and W-5 may be used to predict monthly rainfall of Medak, Khammam and Warangal stations respectively for Central Telangana region.ennullThesis