Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin

In the small watershed of Nanjing section of the Chuhe River Basin,there is a flood intrusion in the upper reaches,and a river tide rising in the lower reaches.Generally,the accuracy of flood forecasting is low.The neural network is widely used in the field of flood forecasting,so the BP neural netw...

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Main Authors: ZHU Yongjun, ZHAN Zhongyu
Format: Article
Language:zho
Published: Editorial Office of Pearl River 2021-01-01
Series:Renmin Zhujiang
Subjects:
Online Access:http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.01.007
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author ZHU Yongjun
ZHAN Zhongyu
author_facet ZHU Yongjun
ZHAN Zhongyu
author_sort ZHU Yongjun
collection DOAJ
description In the small watershed of Nanjing section of the Chuhe River Basin,there is a flood intrusion in the upper reaches,and a river tide rising in the lower reaches.Generally,the accuracy of flood forecasting is low.The neural network is widely used in the field of flood forecasting,so the BP neural network (BPNN) model and the wavelet neural network (WNN) model are used to simulate the flood forecasting of Nanjing section of the Chuhe River Basin.From the simulation indicators of the WNN model,it can be seen that the relative error of the total flow and the peak flow are less than 10%,the relative error of the peak time is less than 2 h,the root mean square error is less than 100,and the correlation coefficient is greater than 0.90,which are better than those of the BPNN model.The results show that the WNN model has higher simulation accuracy and faster convergence speed than the BPNN model,and can provide reference for subsequent flood forecasting in Nanjing section of Chuhe River Basin.
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spelling doaj-art-f146d2233bf742d2bfcd4a7177b45ae52025-01-15T02:29:12ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247647964Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River BasinZHU YongjunZHAN ZhongyuIn the small watershed of Nanjing section of the Chuhe River Basin,there is a flood intrusion in the upper reaches,and a river tide rising in the lower reaches.Generally,the accuracy of flood forecasting is low.The neural network is widely used in the field of flood forecasting,so the BP neural network (BPNN) model and the wavelet neural network (WNN) model are used to simulate the flood forecasting of Nanjing section of the Chuhe River Basin.From the simulation indicators of the WNN model,it can be seen that the relative error of the total flow and the peak flow are less than 10%,the relative error of the peak time is less than 2 h,the root mean square error is less than 100,and the correlation coefficient is greater than 0.90,which are better than those of the BPNN model.The results show that the WNN model has higher simulation accuracy and faster convergence speed than the BPNN model,and can provide reference for subsequent flood forecasting in Nanjing section of Chuhe River Basin.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.01.007wavelet neural networkflood forecastingChuhe River BasinNanjing city
spellingShingle ZHU Yongjun
ZHAN Zhongyu
Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
Renmin Zhujiang
wavelet neural network
flood forecasting
Chuhe River Basin
Nanjing city
title Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
title_full Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
title_fullStr Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
title_full_unstemmed Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
title_short Application of WNN model for Flood Forecasting in Nanjing Section of Chuhe River Basin
title_sort application of wnn model for flood forecasting in nanjing section of chuhe river basin
topic wavelet neural network
flood forecasting
Chuhe River Basin
Nanjing city
url http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.01.007
work_keys_str_mv AT zhuyongjun applicationofwnnmodelforfloodforecastinginnanjingsectionofchuheriverbasin
AT zhanzhongyu applicationofwnnmodelforfloodforecastinginnanjingsectionofchuheriverbasin