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Artificial Neural Network for Markov Chaining of Rainfall Over India

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  • Kavita Pabreja

    (Maharaja Surajmal Institute, GGSIP University, India)

Abstract

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

Suggested Citation

  • Kavita Pabreja, 2020. "Artificial Neural Network for Markov Chaining of Rainfall Over India," International Journal of Business Analytics (IJBAN), IGI Global, vol. 7(3), pages 71-84, July.
    • Handle: RePEc:igg:jban00:v:7:y:2020:i:3:p:71-84
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2020070105
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