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Stochastic process, Seasonal periods, Stochastic variate difference methods, Maximum likelihood estimation


The stochastic variate difference method generates downscaled data in the hydrology and engineering seasonal periods. The new method is developed in the stochastic linear fitted values to fit a straight line to observed data. The paper explains in detail the standard measures such as mean, standard deviation, skewness and kurtosis of the downscaling data in hydrology. The new model - MLE by Gaussian distribution - is a method that was used to find the values to best fit the data sets. Water is one of the sensitive environmental parameters of the hydrological processes. Therefore, the study of water resources exploitation sustainable to the environment is important. Water resources development is an important part of Andhra Pradesh and poses a key issue in the management strategy. This work provides a methodological approach for prediction of the discharge water level for further development and management practices. The present paper predicts future values using stochastic variate difference method for specified fitted values by using downscaling method on daily data.