Application of BP neural network in classified runoff simulation
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Abstract:
The BP neural net wo rk model method for runo ff modeling was int roduced and the mo del was a pplied to simulat e the daily runo ff in a w atershed of Yichang in H ubei Prov ince. First, based on the tempo ral distributio n of rainfall2r uno ff in the study area, t he modeling approaches fo r the w et and dry seasons w ere developed separ ately . Second, the main facto rs affect ing the r un2 o ff wer e analyzed, and t he input v ariables of the model included the r unoff o f f ive previous days, rainfall of three previous days, cur rent rainfall, and cur rent evapotr anspir ation. Thir d, the appr opriat e model str ucture and learning eff iciency parameters wer e determ ined thro ug h trial2and2er ro r tests. Finally, det erminacy coefficient w as used to assess the accuracy o f simulation results. The results show ed t hat the BP neural netw o rk models of the wet and dry seasons over come the disadvantages of low accuracy in pr evio us models w hen simulat ing the ext reme event s, and the BP neur al netwo rk model can simulate the high and low r unoff co nditions wit h hig h accuracy .
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