International Journal of Computational Intelligence Systems

<Previous Article In Issue
Next Article In Issue>
Volume 12, Issue 2, 2019, Pages 1512 - 1525

An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction

Authors
Yuzhong Peng1, 2, *, Huasheng Zhao3, Hao Zhang2, Wenwei Li2, Xiao Qin1, Jianping Liao1, Zhiping Liu3, Jie Li4
1Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Nanning Normal University, Nanning 530001, China
2School of Computer science, Fudan University, Shanghai 200433, China
3Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022, China
4Department of Mathematical and Computer Sciences, Guangxi Science & Technology Normal University, Liuzhou 546100, China
*Corresponding author. Email: jedison@163.com
Corresponding Author
Yuzhong Peng
Received 21 April 2019, Accepted 19 November 2019, Available Online 4 December 2019.
DOI
10.2991/ijcis.d.191126.001How to use a DOI?
Keywords
Extreme Learning Machine; Gene Expression Programming; Quantitative precipitation prediction; Rainfall prediction; Soft computing
Abstract

Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches. This work presents a scheme to reduce the risk of Extreme Learning Machine (ELM) modeling error using Gene Expression Programming (GEP) to improve the prediction performance, and develops an ELM-GEP hybrid model for regional daily quantitative precipitation prediction. In this study, firstly, we use ELM for modeling the data sample of daily rainfall to construct a main model. Secondly, we use GEP for modeling the error of the main model as a compensation of the main model to reduce the prediction error. We conducted eight experiments of two different types of daily precipitation prediction problems using five metrics to evaluate our proposed model performance. Experimental results show that our model is comparable or even superior to five state-of-the-art models with high reliability in terms of all metrics on all datasets. It indicates that the proposed method is a promising alternative prediction tool for higher accuracy and credibility of regional daily precipitation prediction.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1512 - 1525
Publication Date
2019/12/04
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191126.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Yuzhong Peng
AU  - Huasheng Zhao
AU  - Hao Zhang
AU  - Wenwei Li
AU  - Xiao Qin
AU  - Jianping Liao
AU  - Zhiping Liu
AU  - Jie Li
PY  - 2019
DA  - 2019/12/04
TI  - An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 1512
EP  - 1525
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191126.001
DO  - 10.2991/ijcis.d.191126.001
ID  - Peng2019
ER  -