南水北调与水利科技Issue(5)：109-112,123,5.DOI:10.13476/j.cnki.nsbdqk.2014.05.025
利用 BP 神经网络模型进行分类径流模拟
Application of BP neural network in classified runoff simulation
摘要
Abstract
The BP neural net work model method for runoff modeling was int roduced and the model was applied to simulat e the daily runoff in a watershed of Yichang in Hubei Province. First, based on the temporal distribution of rainfall-runoff in the study area, the modeling approaches for the wet and dry seasons w ere developed separately. Second, the main factors affect ing the run-off were analyzed, and t he input variables of the model included the runoff offive previous days, rainfall of three previous days, current rainfall, and current evapotranspiration. Third, the appropriat e model structure and learning eff iciency parameters were determ ined through trial-and-error tests. Finally, det erminacy coefficient was used to assess the accuracy of simulation results. The results showed that the BP neural netw ork models of the wet and dry seasons overcome the disadvantages of low accuracy in previous models w hen simulat ing the extreme events, and the BP neural network model can simulate the high and low runoff conditions wit h high accuracy.关键词
BP 人工神经网络/径流模拟/水文模型/日径流/模拟精度Key words
BP artificial neural netw ork/runoff modeling/hydrological model/daily runoff/simulation accuracy分类
天文与地球科学引用本文复制引用
余楚,吕敦玉..利用 BP 神经网络模型进行分类径流模拟[J].南水北调与水利科技,2014,(5):109-112,123,5.基金项目
中国地质科学院水文地质环境地质研究所基本科研业务费专项( SK201312) （ SK201312）
