Forecasting of Rain Amount for Telangana for the Monsoon Year 2021
Anand M. Sharan *
Department of Mechanical Engineering, Faculty of Engineering, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada.
*Author to whom correspondence should be addressed.
Abstract
In this work four methods are used to predict the month-wise rain amounts for Telangana, a drought prone area of India. The work uses four separate but independent methods to arrive at the result. The four methods are: (1) The Root Mean Square (RMS) method, (2) the Artificial Neural Network (ANN) method, (3) The Fast Fourier Transform (FFT) method, and the Time Series method. The predictoio is based on 32 year rainfall history.
The advantage of this way of the prediction of rain amount is that the result is based on evaluation over a wider area of the feasible final actual value. Secondly, the calculations can be carried out about 7 months in advance of the next rainy season to help in planning of the crops to be planted. Similarly, it gives adequate time to hydro power generators also to plan their electricity generation in the coming year.
Keywords: Monsoon rain prediction, rainfall frequency spectrum, drought and famine, crop failure, drinking water shortage.