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Research Article Open access CC BY 4.0

An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices

Sandip Garai, Ranjit Kumar Paul, Debopam Rakshit, Md. Yeasin, A. K. Paul, H. S. Roy, Samir Barman, B. Manjunatha

International Journal of Environment and Climate Change · pp. 137–150 · Published 24 Mar 2023

10.9734/ijecc/2023/v13i51755

Abstract

A novel method for rainfall forecasting has been proposed using Multi Resolution Analysis (MRA). This approach decomposes annual rainfall series and long-term climate indices into component sub-series at different temporal scales, allowing for a more detailed analysis of the factors influencing annual rainfall. Multiple Linear Regression (MLR) is then used to predict annual rainfall, with climate indices sub-series as predictive variables, using a step-wise linear regression algorithm. The proposed model has been tested on Indian annual rainfall data and compared with the traditional MLR model. Results show that the MRA-based model outperforms the traditional model in terms of relative absolute error and correlation coefficient metrics. The proposed method offers several advantages over traditional methods as it can identify underlying factors affecting annual rainfall at different temporal scales, providing more accurate and reliable rainfall forecasts for better water resource management and agricultural planning. In conclusion, the MRA-based approach is a promising tool for improving the accuracy of annual rainfall predictions, and its implementation can lead to better water resource management and agricultural planning.

Climate indices forecasting MLR MRA rainfall time series

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