Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application
In order to improve the accuracy of hydrological forecasting, this paper establishes a support vector machine (SVM) based on the mixture of polynomial kernel and Gauss kernel, optimizes the key parameters and the mixed weight coefficients of mixed kernel SVM by the electrostatic discharge algorithm...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2020-01-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.01.005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841535540880474112 |
|---|---|
| author | LI Xiangrong |
| author_facet | LI Xiangrong |
| author_sort | LI Xiangrong |
| collection | DOAJ |
| description | In order to improve the accuracy of hydrological forecasting, this paper establishes a support vector machine (SVM) based on the mixture of polynomial kernel and Gauss kernel, optimizes the key parameters and the mixed weight coefficients of mixed kernel SVM by the electrostatic discharge algorithm (ESDA), proposes a monthly runoff forecasting model in dry season based on mixed kernel ESDA-SVM, builds the Gauss kernel ESDA-SVM, polynomial kernel ESDA-SVM and ESDA-BP, as contrast forecasting models, as well as conducts the forecasting through the models with the data of the first 24 years and the last 10 years, taking the monthly runoff forecasting of a hydrological station in Yunnan from January to April in dry season as an example. The results show that the absolute average relative errors of forecasting by the mixed kernel ESDA-SVM model for monthly runoff from January to April are 4.09%, 3.32%, 3.51% and 5.64%, respectively. The forecasting accuracy of the mixed kernel ESDA-SVM model is higher than that of the other 3 models such as polynomial ESDA-SVM model. The mixed kernel ESDA-SVM model combines the advantages of polynomial global kernel function and Gauss local kernel function, so it is superior to the contrast models in forecasting accuracy and generalization ability, and has good practical application value. |
| format | Article |
| id | doaj-art-61602b5d007542058e7f768756729b22 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-61602b5d007542058e7f768756729b222025-01-15T02:31:38ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352020-01-014147652297Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its ApplicationLI XiangrongIn order to improve the accuracy of hydrological forecasting, this paper establishes a support vector machine (SVM) based on the mixture of polynomial kernel and Gauss kernel, optimizes the key parameters and the mixed weight coefficients of mixed kernel SVM by the electrostatic discharge algorithm (ESDA), proposes a monthly runoff forecasting model in dry season based on mixed kernel ESDA-SVM, builds the Gauss kernel ESDA-SVM, polynomial kernel ESDA-SVM and ESDA-BP, as contrast forecasting models, as well as conducts the forecasting through the models with the data of the first 24 years and the last 10 years, taking the monthly runoff forecasting of a hydrological station in Yunnan from January to April in dry season as an example. The results show that the absolute average relative errors of forecasting by the mixed kernel ESDA-SVM model for monthly runoff from January to April are 4.09%, 3.32%, 3.51% and 5.64%, respectively. The forecasting accuracy of the mixed kernel ESDA-SVM model is higher than that of the other 3 models such as polynomial ESDA-SVM model. The mixed kernel ESDA-SVM model combines the advantages of polynomial global kernel function and Gauss local kernel function, so it is superior to the contrast models in forecasting accuracy and generalization ability, and has good practical application value.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.01.005runoff forecastingelectrostatic discharge algorithm (ESDA)mixed kernel functionSupport Vector Machine (SVM)parameter optimization |
| spellingShingle | LI Xiangrong Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application Renmin Zhujiang runoff forecasting electrostatic discharge algorithm (ESDA) mixed kernel function Support Vector Machine (SVM) parameter optimization |
| title | Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application |
| title_full | Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application |
| title_fullStr | Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application |
| title_full_unstemmed | Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application |
| title_short | Monthly Runoff Forecasting Model Based on Electrostatic DischargeAlgorithm-Mixed Kernel SVM and Its Application |
| title_sort | monthly runoff forecasting model based on electrostatic dischargealgorithm mixed kernel svm and its application |
| topic | runoff forecasting electrostatic discharge algorithm (ESDA) mixed kernel function Support Vector Machine (SVM) parameter optimization |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.01.005 |
| work_keys_str_mv | AT lixiangrong monthlyrunoffforecastingmodelbasedonelectrostaticdischargealgorithmmixedkernelsvmanditsapplication |