基于新安江模型和BP神经网络的中小河流洪水模拟研究  

Research on the Flood Simulation of Medium and Small Rivers Based on Xin'anjiang Model and BP Neural Network

作　　者：李鑫 刘艳丽[2,3,4] 朱士江[1] 王国庆 金君良[2,3,4] 贺瑞敏[2,3,4] 刘翠善 LI Xin;LIU Yan-li;ZHU Shi-jiang;WANG Guo-qing;JIN Jun-liang;HE Rui-min;LIU Cui-shan(College of Hydraulic and Environmental Engineering,Three Gorges University,Yichang 443000,Hubei Province,China;State Key Laboratory of Hydrology,Water Resources and Water Conservancy Engineering,Nanjing Institute of Water Resources,Nanjing 210098,China;Research Center for Climate Change,Ministry of Water Resources,Nanjing 210029,China;Yangtze River Protection and Green Development Research Institute,Nanjing 210098,China)

机构地区：[1]三峡大学水利与环境学院,湖北宜昌443000 [2]南京水利科学研究院水文水资源与水利工程科学国家重点实验室,南京210098 [3]水利部应对气候变化研究中心,南京210029 [4]长江保护与绿色发展研究院,南京210098

出　　处：《中国农村水利水电》2022年第1期93-97,共5页China Rural Water and Hydropower

基　　金：国家重点研发计划(2018YFC1508104);国家自然科学基金(51679145;91747103)。

摘　　要：为探讨更加符合中小河流域防洪要求的预报方法,并提高洪水预报精度,以屯溪流域为例,结合中小河流实际洪水预报要求,采用以洪峰合格率和峰现时间合格率为主要约束的非等权重的参数率定方法(即目标函数中径流深、洪峰流量、峰现时间合格率和确定性系数的权重分别为(1∶2∶2∶1)对新安江模型进行参数率定,并采用算术平均法耦合新安江模型和BP神经网络模型计算结果,以期提高洪水预报精度。结果表明:以洪峰合格率和峰现时间合格率为主要约束的率定方法的新安江模型是可行的,相对于传统等权重的率定方法,在洪峰和峰现时间预报方面更具优势,符合中小河流防洪要求;新安江模型对洪峰和峰现时间模拟较好,BP神经网络模型对洪峰和径流深的模拟表现较好,采用算数平均法耦合两者的模拟结果,可以提高洪水预报精度。In order to explore the forecasting method which is more in line with the flood control requirements of the middle and small river basin and improve the accuracy of flood forecasting,taking Tunxi Basin as an example,combined with the actual flood forecasting requirements of the middle and small rivers,a non-equal-weight parameter calibration method with the qualified rate of flood peak and the qualified rate of flood peak onset time as the main constraints is adopted(i.e.,the weights of runoff depth,flood peak discharge,qualified rate of peak onset time and deterministic coefficient in the objective function are respectively(1∶2∶2∶1)to calibrate the parameters of the Xin′anjiang River model,and use the arithmetic mean method to couple the calculation results of the Xin′anjiang River model and BP neural network model so as to improve the accuracy of flood prediction.The results show that the method based on the main constraints of flood peak discharge and peak occurrence time is feasible in the flood prediction of Tunxi Basin.Compared with the traditional equal-weight method,the method has more advantages in the flood peak and peak occurrence time prediction,and meets the flood control requirements of small and medium-sized rivers.The Xin′anjiang River model can simulate the flood peak and peak time well,and the BP neural network model can simulate the flood peak and runoff depth well.The arithmetic mean method is used to couple the simulation results of the two models can improve the accuracy of flood prediction.

关 键 词：中小河流 新安江模型 BP神经网络模型 参数率定 耦合模型 

分 类 号：P338[天文地球—水文科学]