Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin
The Poak-Ribiére conjugate gradient back propagation algorithm (PRBP) of numerical optimization technology was used, and 21 rainstorm and flood processes from 1997 to 2022 in the upper reaches of Chongyang River basin were studied. The rainfall volume of six rainfall stations in the upper reaches of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2025-06-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.008 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849424481036009472 |
|---|---|
| author | SI Qi JIN Baoming LU Wangming CHEN Zhaoqing |
| author_facet | SI Qi JIN Baoming LU Wangming CHEN Zhaoqing |
| author_sort | SI Qi |
| collection | DOAJ |
| description | The Poak-Ribiére conjugate gradient back propagation algorithm (PRBP) of numerical optimization technology was used, and 21 rainstorm and flood processes from 1997 to 2022 in the upper reaches of Chongyang River basin were studied. The rainfall volume of six rainfall stations in the upper reaches of Chongyang River basin and the previous discharge of Wuyishan Hydrological Station were regarded as input, and its corresponding discharge was regarded as output; the number of hidden layer units was determined by trial calculation, and then PRBP neural network flood forecasting model of Chongyangxi River Basin was established. The remaining eight floods were used to test and validate the model. The results show that compared with that of the conventional BP neural network model, the convergence speed of the model is faster, and the calculation speed is obviously improved; the deterministic coefficient of the model is greater than 0.87, and the relative error of peak flow of six floods is within 10%. The forecasting accuracy meets the requirements, which can provide a basis for the flood control department to forecast the flood. |
| format | Article |
| id | doaj-art-8977014baf074e919308de555b188328 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-8977014baf074e919308de555b1883282025-08-20T03:30:09ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352025-06-0146687490550443Research on PRBP Neural Network Flood Forecasting Model in Chongyang River BasinSI QiJIN BaomingLU WangmingCHEN ZhaoqingThe Poak-Ribiére conjugate gradient back propagation algorithm (PRBP) of numerical optimization technology was used, and 21 rainstorm and flood processes from 1997 to 2022 in the upper reaches of Chongyang River basin were studied. The rainfall volume of six rainfall stations in the upper reaches of Chongyang River basin and the previous discharge of Wuyishan Hydrological Station were regarded as input, and its corresponding discharge was regarded as output; the number of hidden layer units was determined by trial calculation, and then PRBP neural network flood forecasting model of Chongyangxi River Basin was established. The remaining eight floods were used to test and validate the model. The results show that compared with that of the conventional BP neural network model, the convergence speed of the model is faster, and the calculation speed is obviously improved; the deterministic coefficient of the model is greater than 0.87, and the relative error of peak flow of six floods is within 10%. The forecasting accuracy meets the requirements, which can provide a basis for the flood control department to forecast the flood.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.008PR conjugate gradient methodBP neural networkflood forecastingChongyang River basin |
| spellingShingle | SI Qi JIN Baoming LU Wangming CHEN Zhaoqing Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin Renmin Zhujiang PR conjugate gradient method BP neural network flood forecasting Chongyang River basin |
| title | Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin |
| title_full | Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin |
| title_fullStr | Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin |
| title_full_unstemmed | Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin |
| title_short | Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin |
| title_sort | research on prbp neural network flood forecasting model in chongyang river basin |
| topic | PR conjugate gradient method BP neural network flood forecasting Chongyang River basin |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.008 |
| work_keys_str_mv | AT siqi researchonprbpneuralnetworkfloodforecastingmodelinchongyangriverbasin AT jinbaoming researchonprbpneuralnetworkfloodforecastingmodelinchongyangriverbasin AT luwangming researchonprbpneuralnetworkfloodforecastingmodelinchongyangriverbasin AT chenzhaoqing researchonprbpneuralnetworkfloodforecastingmodelinchongyangriverbasin |