摘要
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both temporal and spatial ＂variabilities, In this article, a rainfall-runoff model using the artificial neural networks （ANN） is proposed for simulating the runoff in storm events. The study uses the data from a coastal forest catchment, located in Seto Inland Sea, Japan, This article studies the accuracy of the short-term rainfall forecast obtained by ANN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rainfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy,
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第1期68-73,共6页
Journal of Chengdu University of Technology: Science & Technology Edition
