Objective: To predict the probability of rainfall based on monthly mean data of rainfall, runoff and evapotranspiration collected from a single gauge station for the last fifty years Data Preprocessing : The fifty years mean monthly data of Rainfall,Runoff and Evapotranspiration is available.The data values of the variables is ranked in a descending way where best or minimum rank is assigned to the largest value and worst or maximum rank was given to smallest value of the variable.The probability of each of the data value of the variables can be calculated with the help of the following formula : P = m/(n+1) The data thus converted become dimensionless and thus normalization is not followed. If the data values have dimensions it is better to remove the scale from the same. This will make the job of the neural network program faster and easier. Input Variables of the Present Study : Input to the model will be Probability of Runoff and Evapotranspiration. Output Variable of the Present S
Publishes Journals on Water and Energy Research
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