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

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 appr...

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Published in:	International journal of computational intelligence systems Vol. 12; no. 2; pp. 1512 - 1525
Main Authors:	Peng, Yuzhong, Zhao, Huasheng, Zhang, Hao, Li, Wenwei, Qin, Xiao, Liao, Jianping, Liu, Zhiping, Li, Jie
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.01.2019 Springer Nature B.V Springer
Subjects:	Artificial neural networks Errors Extreme Learning Machine Gene expression Gene Expression Programming Machine learning Modelling Precipitation Quantitative precipitation prediction Rainfall Rainfall prediction Regional development Research Article Soft computing Weather Gene Expression Programming Extreme Learning Machine Soft computing Quantitative precipitation prediction Rainfall prediction
ISSN:	1875-6891, 1875-6883, 1875-6883
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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.
AbstractList	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.
Author	Peng, Yuzhong Liao, Jianping Zhao, Huasheng Li, Jie Qin, Xiao Liu, Zhiping Zhang, Hao Li, Wenwei
Author_xml	– sequence: 1 givenname: Yuzhong surname: Peng fullname: Peng, Yuzhong email: jedison@163.com organization: Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Nanning Normal University, School of Computer science, Fudan University – sequence: 2 givenname: Huasheng surname: Zhao fullname: Zhao, Huasheng organization: Guangxi Research Institute of Meteorological Disasters Mitigation – sequence: 3 givenname: Hao surname: Zhang fullname: Zhang, Hao organization: School of Computer science, Fudan University – sequence: 4 givenname: Wenwei surname: Li fullname: Li, Wenwei organization: School of Computer science, Fudan University – sequence: 5 givenname: Xiao surname: Qin fullname: Qin, Xiao organization: Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Nanning Normal University – sequence: 6 givenname: Jianping surname: Liao fullname: Liao, Jianping organization: Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Nanning Normal University – sequence: 7 givenname: Zhiping surname: Liu fullname: Liu, Zhiping organization: Guangxi Research Institute of Meteorological Disasters Mitigation – sequence: 8 givenname: Jie surname: Li fullname: Li, Jie organization: Department of Mathematical and Computer Sciences, Guangxi Science & Technology Normal University
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SubjectTerms	Artificial neural networks Errors Extreme Learning Machine Gene expression Gene Expression Programming Machine learning Modelling Precipitation Quantitative precipitation prediction Rainfall Rainfall prediction Regional development Research Article Soft computing Weather
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Title	An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction
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