Monthly rainfall–runoff modelling using artificial neural networks

Abstract

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, Paraná, 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.