Radial Basis Function Neural Network for Modeling Rating Curves

作者：K. P. Sudheer, Sharad K. Jain · 发表于：Journal of Hydrologic Engineering · 年份：2003 · DOI：10.1061/(asce)1084-0699(2003)8:3(161) · 被引用次数：144 · 研究领域：Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies、Dam Engineering and Safety

The establishment of a rating curve is an important problem in hydrology. Generally, a regression approach is applied to establish the relationship between stage and discharge. However, this approach fails in the cases where hysteresis is present in the data. The aim of the study is to investigate the potential of employing radial basis function (RBF) type neural networks for modeling stage-discharge relationships at gauging stations and to compare different types of networks. The results are promising and suggest that the neural network approach is highly viable. A comparison of the RBF models with backpropagation type neural networks reveals that the former is superior in performance for rating curves exhibiting hysteresis.