Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model
To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,th...
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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.1001-9235.2022.08.015 |
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| _version_ | 1841535840559300608 |
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| author | ZHANG Yajie CUI Dongwen |
| author_facet | ZHANG Yajie CUI Dongwen |
| author_sort | ZHANG Yajie |
| collection | DOAJ |
| description | To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,the monthly runoff with good correlation before the forecast month is selected as the influencing factor of forecasts,and the influencing factor is reduced in dimension by principal component analysis (PCA).The kernel width factor and hyperparameters of RVM are optimized by the GEO algorithm,and the GEO-RVM model is built to forecast the monthly runoff of the station during the dry season from November to April of the following year.Moreover,the forecast results are compared with those of the GEO-based support vector machine (SVM) model (GEO-SVM).The results demonstrate that the average relative errors of the GEO-RVM model for the monthly runoff forecasts from November to April of the following year are 8.59%,7.34%,5.97%,6.07%,5.99%,and 5.04%,respectively,which means the accuracy is better than that of the GEO-SVM model.The GEO algorithm can effectively optimize the kernel width factor and hyperparameters of RVM,and the GEO-RVM model has better forecast accuracy,which can be used for monthly runoff forecasting during dry seasons. |
| format | Article |
| id | doaj-art-23dfa8b2c8184281a14e05680f627752 |
| 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-23dfa8b2c8184281a14e05680f6277522025-01-15T02:26:58ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352022-01-014347644407Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM ModelZHANG YajieCUI DongwenTo improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,the monthly runoff with good correlation before the forecast month is selected as the influencing factor of forecasts,and the influencing factor is reduced in dimension by principal component analysis (PCA).The kernel width factor and hyperparameters of RVM are optimized by the GEO algorithm,and the GEO-RVM model is built to forecast the monthly runoff of the station during the dry season from November to April of the following year.Moreover,the forecast results are compared with those of the GEO-based support vector machine (SVM) model (GEO-SVM).The results demonstrate that the average relative errors of the GEO-RVM model for the monthly runoff forecasts from November to April of the following year are 8.59%,7.34%,5.97%,6.07%,5.99%,and 5.04%,respectively,which means the accuracy is better than that of the GEO-SVM model.The GEO algorithm can effectively optimize the kernel width factor and hyperparameters of RVM,and the GEO-RVM model has better forecast accuracy,which can be used for monthly runoff forecasting during dry seasons.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.08.015monthly runoff forecastrelevance vector machinegolden eagle optimization algorithmdata dimension reductionparameter optimizationdry season |
| spellingShingle | ZHANG Yajie CUI Dongwen Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model Renmin Zhujiang monthly runoff forecast relevance vector machine golden eagle optimization algorithm data dimension reduction parameter optimization dry season |
| title | Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model |
| title_full | Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model |
| title_fullStr | Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model |
| title_full_unstemmed | Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model |
| title_short | Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model |
| title_sort | research on monthly runoff forecast in dry seasons based on geo rvm model |
| topic | monthly runoff forecast relevance vector machine golden eagle optimization algorithm data dimension reduction parameter optimization dry season |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.08.015 |
| work_keys_str_mv | AT zhangyajie researchonmonthlyrunoffforecastindryseasonsbasedongeorvmmodel AT cuidongwen researchonmonthlyrunoffforecastindryseasonsbasedongeorvmmodel |