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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References	Cheng, Yang (CR12) 2016; 31 CR19 Dufek, Augusto, Dias, Barbosa (CR24) 2017; 106 CR16 CR15 Zhao, Jin, Huang, Jin (CR27) 2014; 73 Venkatesh, Devi, Arulmozhivarman (CR29) 2016; 13 CR13 CR11 Partal, Kişi (CR17) 2007; 342 Sharma, Goyal (CR23) 2016; 26 CR32 Nasseri, Asghari, Abedini (CR9) 2008; 35 Kim, Seo, Lee (CR28) 2016; 154 Huang, Jiang (CR39) 2012; 42 Kashiwao, Nakayama, Ando, Ikeda, Lee, Bahadori (CR10) 2017; 56 Huang, Zhou, Ding, Zhang (CR36) 2012; 42 Wu, Chau (CR18) 2013; 26 Chu, He (CR5) 2010; 14 Acharya, Shrivastava, Panigrahi, Mohanty (CR14) 2014; 43 Huang, Zhu, Siew (CR31) 2006; 70 CR2 Kuligowski, Barros (CR8) 1998; 13 CR4 Ranjannayak, Mahapatra, Mishra (CR26) 2014; 72 Sheng, Gao, Xue (CR1) 2006; 94 Prakash, Mahesh, Gairola, Buyantogtokh (CR3) 2012; 2 Ferreira (CR33) 2001; 13 CR7 CR25 CR22 Sun, Huang, Cheung, Liu, Huang (CR40) 2005; 2 CR21 CR20 Jedrzejowicz, Jedrzejowicz (CR35) 2018; 2018 Jin, Zhu, Huang, Zhao, Lin, Jin (CR38) 2015; 119 Roushangar, Alizadeh, Nourani (CR37) 2018; 20 Marques, Ferreira, Rocha, Castanheira, Melo-Gonçalves, Vaz, Dias (CR6) 2006; 31 Unnikrishnan, Jothiprakash (CR30) 2018; 20 Peng, Yuan, Qin, Huang, Shi (CR34) 2014; 137
References_xml	– ident: CR22 – volume: 137 start-page: 293 year: 2014 end-page: 301 ident: CR34 article-title: An improved gene expression programming approach for symbolic regression problems publication-title: Neurocomputing. – volume: 106 start-page: 139 year: 2017 end-page: 149 ident: CR24 article-title: Application of evolutionary computation on ensemble forecast of quantitative precipitation publication-title: Comput. Geosci. – volume: 20 start-page: 645 year: 2018 end-page: 667 ident: CR30 article-title: Data-driven multi-time-step ahead daily rainfall forecasting using singular spectrum analysis-based data pre-processing publication-title: J. Hydroinform. – ident: CR4 – ident: CR2 – ident: CR16 – volume: 26 start-page: 641 year: 2016 end-page: 655 ident: CR23 article-title: A comparison of three soft computing techniques, bayesian regression, support vector regression, and wavelet regression, for monthly rainfall forecast publication-title: J. Intell. Syst. – volume: 56 start-page: 317 year: 2017 end-page: 330 ident: CR10 article-title: A neural network-based local rainfall prediction system using meteorological data on the internet: a case study using data from the japan meteorological agency publication-title: Appl. Soft Comput. – volume: 14 start-page: 659 year: 2010 end-page: 669 ident: CR5 article-title: Long-range prediction of hawaiian winter rainfall using canonical correlation analysis publication-title: Int. J. Climatol. – volume: 154 start-page: 1231 year: 2016 end-page: 1236 ident: CR28 article-title: Modeling of rainfall by combining neural computation and wavelet technique publication-title: Procedia Eng. – volume: 72 start-page: 32 year: 2014 end-page: 40 ident: CR26 article-title: A survey on rainfall prediction using artificial neural network publication-title: Int. J. Comput. Appl. – volume: 35 start-page: 1415 year: 2008 end-page: 1421 ident: CR9 article-title: Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network publication-title: Expert Syst. Appl. – volume: 13 start-page: 417 year: 2016 end-page: 427 ident: CR29 article-title: Performance comparison of artificial neural network models for daily rainfall prediction publication-title: Int. J. Automat. Comput. – volume: 13 start-page: 87 year: 2001 end-page: 129 ident: CR33 article-title: Gene expression programming: a new adaptive algorithm for solving problems publication-title: Complex Syst. – ident: CR25 – volume: 73 start-page: 427 year: 2014 end-page: 437 ident: CR27 article-title: An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble publication-title: Nat. Hazards. – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: CR31 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing. – volume: 42 start-page: 1489 year: 2012 end-page: 500 ident: CR39 article-title: A general cpl-ads methodology for fixing dynamic parameters in dual environments publication-title: IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. – volume: 2018 start-page: 1 year: 2018 end-page: 13 ident: CR35 article-title: Incremental gene expression programming classifier with metagenes and data reduction publication-title: Complexity. – ident: CR21 – ident: CR19 – volume: 31 start-page: 1172 year: 2006 end-page: 1179 ident: CR6 article-title: Singular spectrum analysis and forecasting of hydrological time series publication-title: Phy. Chem. Earth. – volume: 94 start-page: 167 year: 2006 end-page: 183 ident: CR1 article-title: Short-range prediction of a heavy precipitation event by assimilating chinese cinrad-sa radar reflectivity data using complex cloud analysis publication-title: Meteorol. Atmos. Phy. – volume: 119 start-page: 791 year: 2015 end-page: 807 ident: CR38 article-title: A nonlinear statistical ensemble model for short-range rainfall prediction publication-title: Theor. Appl. Climatol. – ident: CR15 – volume: 26 start-page: 997 year: 2013 end-page: 1007 ident: CR18 article-title: Prediction of rainfall time series using modular soft computingmethods publication-title: Eng. Appl. Artif. Intell. – volume: 342 start-page: 199 year: 2007 end-page: 212 ident: CR17 article-title: Wavelet and neuro-fuzzy conjunction model for precipitation forecasting publication-title: J. Hydrol. – ident: CR13 – ident: CR11 – ident: CR32 – volume: 2 start-page: 108 year: 2005 end-page: 112 ident: CR40 article-title: Using fcmc, fvs, and pca techniques for feature extraction of multi-spectral images publication-title: IEEE Geosci. Remote Sensing Lett. – volume: 2 start-page: 138 year: 2012 end-page: 152 ident: CR3 article-title: A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data publication-title: Int. J. Hydrol. Sci. Technol. – ident: CR7 – volume: 13 start-page: 1194 year: 1998 end-page: 1204 ident: CR8 article-title: Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks publication-title: Weather Forecast. – volume: 31 start-page: 915 year: 2016 end-page: 925 ident: CR12 article-title: A novel rainfall forecast model based on the integrated non-linear attribute selection method and support vector regression publication-title: J. Intell. Fuzzy Syst. – volume: 42 start-page: 513 year: 2012 end-page: 529 ident: CR36 article-title: Extreme learning machine for regression and multiclass classification publication-title: IEEE Trans. Syst. Man Cybern. B Cybern. – volume: 43 start-page: 1303 year: 2014 end-page: 1310 ident: CR14 article-title: Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine publication-title: Clim. Dyn. – volume: 20 start-page: 69 year: 2018 end-page: 87 ident: CR37 article-title: Improving capability of conceptual modeling of watershed rainfallcrunoff using hybrid wavelet-extreme learning machine approach publication-title: J. Hydroinform. – ident: CR20
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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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