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Prediction of rainfall time series using soft computing techniques

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Abstract

Past studies indicate that increasing temperatures would accelerate the Earth’s water cycle and in turn would increase the evaporation rate. Increased evaporation will result in more frequent and intense storms; hence, most researchers focus on climate change and its effect on Earth, particularly the precipitation. In the last two decades, the Udaipur district, India, faces water scarcity and flooding situations twice. The present study focuses on the prediction of rainfall using the most advanced soft computing techniques (SCT) such as multivariate adaptive regression splines (MARS), classification and regression trees (CART), and gene expression programming (GEP) in India’s Udaipur district. The performance of these SCT was evaluated to test the capability to predict the rainfall. Results showed that the MARS model for rainfall prediction showed better performance than the GEP model.

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source: Census of India, 2011)

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Acknowledgements

The author wishes to thank Professor Hazi Md Azamathulla, University of the West Indies, St. Augustine Campus for his suggestions in preparation of this manuscript and review. The authors wish to acknowledge the extensive support of the Udaipur Meteorological Service in providing the climate data for this study.

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  1. Department of Geography, M. J. K. College, Bettiah, A Constituent Unit of B.R.A. Bihar University, Muzaffarpur, Bihar, India

    Barkha Chaplot

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  1. Barkha Chaplot
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Correspondence to Barkha Chaplot.

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Chaplot, B. Prediction of rainfall time series using soft computing techniques. Environ Monit Assess 193, 721 (2021). https://doi.org/10.1007/s10661-021-09388-1

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