Medium-long-term Forecast of the Runoff of the Xinfengjiang Reservoir in Dry Season Based on the Random Forest and RBF Artificial Neural Network
Based on the random forest and RBF artificial neural network, this paper establishes themedium-long-term forecast model for the runoff of Xinfengjiang reservoir in dry season, filtersthe forecast factors from 74 hydrometeorological features and pre-rainfall and runoff by therandom forest, and then f...
Saved in:
| Main Authors: | , , , |
|---|---|
| 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.1001-9235.2020.05.005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Based on the random forest and RBF artificial neural network, this paper establishes themedium-long-term forecast model for the runoff of Xinfengjiang reservoir in dry season, filtersthe forecast factors from 74 hydrometeorological features and pre-rainfall and runoff by therandom forest, and then forecasts the monthly runoff of the Xinfengjiang reservoir in dry seasonby RBF neural network with the filtered forecast factor as the input layer. The results show thatthe medium-long-term forecast model for the runoff in dry season based on the random forest andRBF artificial neural network model has higher accuracy, among which the average pass rate intraining period is 91.24%, with the average relative error of 7.80%; while the average pass ratein the test period is 67.31%, with the average relative error of 26.73%, so the model has highreliability. The results can be used as an important reference for the forecast of runoff in theDongjiang River basin in dry season. |
|---|---|
| ISSN: | 1001-9235 |