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Precipitation prediction in several Chinese regions using machine learning methods

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Abstract

The severity of global climate change is exemplified by the significant increase in extreme precipitation events, leading to an urgent need for accurate rainfall prediction models to mitigate flood disasters that adversely affect economic and social development. With the rapid progress of machine learning in the big data era, novel solutions to regression problems are being proposed. In this paper, we try to construct and evaluate different rainfall prediction models based on specific humidity, relative humidity, horizontal and vertical water vapor flux, and lifting index as variables, using four classic machine learning algorithms: linear regression, random forest regression, support vector regression, and Bayesian ridge regression. The grid search method is employed for hyperparameter tuning, significantly improving the models' prediction accuracy and generalization ability. Evaluation of the predictive performance of the models on nine typical regions in China, including Zhengzhou, Beijing, and Chengdu, demonstrates that the random forest regression model has the highest predictive accuracy, with an average fitting degree of 0.8 or above, followed by support vector regression and Bayesian ridge regression models. Conversely, the linear regression model may have the poorest predictive performance. Therefore, the random forest regression model is recommended for future precipitation prediction, providing a valuable solution to various regression problems. The appropriate selection of variables for prediction and grid search for hyperparameter tuning are possibly the highlights of this paper.

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The data sets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The author would like to acknowledge the National Natural Science Foundation of China (NO. 11972327) for the financial support for this paper. They also would like to thank the anonymous reviewers for their helpful and kind suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (NO. 11972327).

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, 450001, Henan, China

    Yuyao Wang & Lijun Pei

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Jiachen Wang

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  1. Yuyao Wang
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  2. Lijun Pei
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  3. Jiachen Wang
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Contributions

LP has made the substantial contributions to the conception and design of the work, and interpretation of the predication results, revised critically the important content, approved the final version to be published; YW has carried out the total analysis and predication, and drafted the manuscript; JW has collected the data for analysis and prediction, checked and polished the draft.

Corresponding author

Correspondence to Lijun Pei.

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Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following Financial interests/personal relationships which may be considered as potential competing interests: No.

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Wang, Y., Pei, L. & Wang, J. Precipitation prediction in several Chinese regions using machine learning methods. Int. J. Dynam. Control 12, 1180–1196 (2024). https://doi.org/10.1007/s40435-023-01250-1

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  • DOI: https://doi.org/10.1007/s40435-023-01250-1

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