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International Conference on Extreme Learning Machine

ELM 2018: Proceedings of ELM 2018 pp 328–335Cite as

A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 11))

Abstract

Precise daily rainfall forecasting play a very significant role in modern society that it can not only help for planning of people’s day-to-day activities, agriculture and business, but also assist water resource management in the region to warn or alleviate the effect of drought or flood disaster. However, various inherently complex meteorological factors and dynamic behavior influence the rainfall, with result that it is very difficult to accurately forecast daily rainfall. This study presents a soft computing modeling method based on Extreme Learning Machine (ELM) and Gene Expression Programming (GEP) to enhance the forecast performance. The proposed mode is compared with other five rainfall forecasting models to assess its performance for rainfall forecasting by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results show that the proposed method outperforms other models in terms of accuracy.

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Acknowledgements

We highly appreciate that this work is supported by the National Science Foundation of China Grant #61562008 and #41575051, and Guangxi scientific research and technology development project# 1598019-1 and #AB16450013, and the National Science Foundation of Guangxi Grant #2017GXNSFAA198228, #2017GXNSFBA198153, #2016GXNSFAA380209 and #2014GXNSFDA118037, and The basic ability promotion project of young and middle-aged teachers in Guangxi universities #2017KY0896, and the open research project of Guangxi Colleges and Universities Key Laboratory of Data Science, and the grant of “Bagui Scholars” Program of Guangxi Province of China.

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Authors and Affiliations

  1. Key Lab of Scientific Computing, and Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University, Nanning, 530001, China

    Yuzhong Peng, Xiao Qin & Jianping Liao

  2. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning, 530022, China

    Huasheng Zhao & Zhiping Liu

  3. Department of Mathematical and Computer Sciences, Guangxi Science and Technology Normal University, Liuzhou, 546100, China

    Jie Li

Authors
  1. Yuzhong Peng
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  2. Huasheng Zhao
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  3. Jie Li
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  4. Xiao Qin
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  5. Jianping Liao
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  6. Zhiping Liu
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Corresponding author

Correspondence to Jie Li .

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Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Xiasha, Hangzhou, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Cybersecurity Research, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

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Peng, Y., Zhao, H., Li, J., Qin, X., Liao, J., Liu, Z. (2020). A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_35

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