期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 56, 期 3, 页码 349-361出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2011.559949
关键词
artificial neural networks; rainfall-runoff models; empirical models; Brazil
Rainfall-runoff models usually present good results, but parameter calibration sometimes is tedious and subjective, and in many cases it depends on additional data surveys in the field. An alternative to the conceptual models is provided by empirical models, which relate input and output by means of an arbitrary mathematical function that bears no direct relationship to the physical characteristics of the rainfall-runoff process. This category includes the artificial neural networks (ANNs), whose implementation is the main focus of this paper. This study evaluated the capacity of ANNs to model with accuracy the monthly rainfall-runoff process. The case study was performed in the Jangada River basin, Parana, Brazil. The results of the three ANNs that produced the best results were compared to those of a conceptual model at monthly time scale, IPHMEN. The ANNs presented the best results with highest correlation coefficients and Nash-Sutcliffe statistics and the smallest difference of volume. Citation Machado, F., Mine, M., Kaviski, E. Fill, H. (2011) Monthly rainfall-runoff modelling using artificial neural networks. Hydrol. Sci. J. 56(3), 349-361.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据