Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Authors

  • Xing Zhang Faculty of Mathematics and Computer Science, Guangxi Normal University of Science and Technology, Laibin, Guangxi, 546100, China
  • Jiaquan Zhou

    Faculty of Mathematics and Computer Science, Guangxi Normal University of Science and Technology, Laibin, Guangxi, 546100, China
  • Jiansheng Wu Faculty of Mathematics and Computer Science, Guangxi Normal University of Science and Technology, Laibin, Guangxi, 546100, China
  • Lingmei Wu Faculty of Mathematics and Computer Science, Guangxi Normal University of Science and Technology, Laibin, Guangxi, 546100, China
  • Liqiang Zhang Faculty of Mathematics and Computer Science, Guangxi Normal University of Science and Technology, Laibin, Guangxi, 546100, China

DOI:

https://doi.org/10.30564/jcsr.v5i1.5303

Abstract

Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction.

Keywords:

Mean generating function; Principal component analysis; Extreme learning machine ensemble; Precipitation prediction

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How to Cite

Zhang, X., Zhou, J., Wu, J., Wu, L., & Zhang, L. (2023). Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble. Journal of Computer Science Research, 5(1), 1–12. https://doi.org/10.30564/jcsr.v5i1.5303

Issue

Vol. 5 , Iss. 1 (January 2023)

Article Type

Article

License

Copyright © 2023 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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