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Artificial Neural Network Modeling of the Rainfall‐Runoff Process
Author(s) -
Hsu Kuolin,
Gupta Hoshin Vijai,
Sorooshian Soroosh
Publication year - 1995
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/95wr01955
Subject(s) - artificial neural network , nonlinear system , autoregressive model , computer science , watershed , representation (politics) , autoregressive–moving average model , hydrological modelling , process (computing) , series (stratigraphy) , mathematical model , time series , hydrology (agriculture) , mathematics , machine learning , econometrics , statistics , engineering , geology , geotechnical engineering , paleontology , physics , quantum mechanics , climatology , politics , political science , law , operating system
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. This study presents a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three‐layer feed forward ANN models and demonstrates the potential of such models for simulating the nonlinear hydrologic behavior of watersheds. The nonlinear ANN model approach is shown to provide a better representation of the rainfall‐runoff relationship of the medium‐size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressive moving average with exogenous inputs) time series approach or the conceptual SAC‐SMA (Sacramento soil moisture accounting) model. Because the ANN approach presented here does not provide models that have physically realistic components and parameters, it is by no means a substitute for conceptual watershed modeling. However, the ANN approach does provide a viable and effective alternative to the ARMAX time series approach for developing input‐output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed.