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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2019-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125925031/view |
| _version_ | 1811252351632146432 |
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| author | Yuzhong Peng Huasheng Zhao Hao Zhang Wenwei Li Xiao Qin Jianping Liao Zhiping Liu Jie Li |
| author_facet | Yuzhong Peng Huasheng Zhao Hao Zhang Wenwei Li Xiao Qin Jianping Liao Zhiping Liu Jie Li |
| author_sort | Yuzhong Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T16:33:28Z |
| format | Article |
| id | doaj.art-0d00e41cdca547b7bde9817345681128 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-12T16:33:28Z |
| publishDate | 2019-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-0d00e41cdca547b7bde98173456811282022-12-22T03:25:03ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832019-12-0112210.2991/ijcis.d.191126.001An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation PredictionYuzhong PengHuasheng ZhaoHao ZhangWenwei LiXiao QinJianping LiaoZhiping LiuJie LiAccurate 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.https://www.atlantis-press.com/article/125925031/viewExtreme Learning MachineGene Expression ProgrammingQuantitative precipitation predictionRainfall predictionSoft computing |
| spellingShingle | Yuzhong Peng Huasheng Zhao Hao Zhang Wenwei Li Xiao Qin Jianping Liao Zhiping Liu Jie Li An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction International Journal of Computational Intelligence Systems Extreme Learning Machine Gene Expression Programming Quantitative precipitation prediction Rainfall prediction Soft computing |
| title | An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction |
| title_full | An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction |
| title_fullStr | An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction |
| title_full_unstemmed | An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction |
| title_short | An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction |
| title_sort | extreme learning machine and gene expression programming based hybrid model for daily precipitation prediction |
| topic | Extreme Learning Machine Gene Expression Programming Quantitative precipitation prediction Rainfall prediction Soft computing |
| url | https://www.atlantis-press.com/article/125925031/view |
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