Medium- and Long-term Runoff Prediction Based on SMA-LSSVM
Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction m...
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Editorial Office of Pearl River
2022-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.015 |
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| _version_ | 1841535882620829696 |
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| author | TIAN Jinghuan LI Congxin LI Ang |
| author_facet | TIAN Jinghuan LI Congxin LI Ang |
| author_sort | TIAN Jinghuan |
| collection | DOAJ |
| description | Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction. |
| format | Article |
| id | doaj-art-a92adcd3fd824d9d9d654a04edb67408 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-a92adcd3fd824d9d9d654a04edb674082025-01-15T02:26:39ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352022-01-014347643839Medium- and Long-term Runoff Prediction Based on SMA-LSSVMTIAN JinghuanLI CongxinLI AngMedium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.015slime mold algorithm (SMA)least squares support vector machine (LSSVM)runoff predictionparameter optimization |
| spellingShingle | TIAN Jinghuan LI Congxin LI Ang Medium- and Long-term Runoff Prediction Based on SMA-LSSVM Renmin Zhujiang slime mold algorithm (SMA) least squares support vector machine (LSSVM) runoff prediction parameter optimization |
| title | Medium- and Long-term Runoff Prediction Based on SMA-LSSVM |
| title_full | Medium- and Long-term Runoff Prediction Based on SMA-LSSVM |
| title_fullStr | Medium- and Long-term Runoff Prediction Based on SMA-LSSVM |
| title_full_unstemmed | Medium- and Long-term Runoff Prediction Based on SMA-LSSVM |
| title_short | Medium- and Long-term Runoff Prediction Based on SMA-LSSVM |
| title_sort | medium and long term runoff prediction based on sma lssvm |
| topic | slime mold algorithm (SMA) least squares support vector machine (LSSVM) runoff prediction parameter optimization |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.015 |
| work_keys_str_mv | AT tianjinghuan mediumandlongtermrunoffpredictionbasedonsmalssvm AT licongxin mediumandlongtermrunoffpredictionbasedonsmalssvm AT liang mediumandlongtermrunoffpredictionbasedonsmalssvm |