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Titolo:
ARTIFICIAL NEURAL-NETWORK MODELING OF THE RAINFALL-RUNOFF PROCESS
Autore:
HSU KL; GUPTA HV; SOROOSHIAN S;
Indirizzi:
UNIV ARIZONA,DEPT HYDROL & WATER RESOURCES TUCSON AZ 85721
Titolo Testata:
Water resources research
fascicolo: 10, volume: 31, anno: 1995,
pagine: 2517 - 2530
SICI:
0043-1397(1995)31:10<2517:ANMOTR>2.0.ZU;2-D
Fonte:
ISI
Lingua:
ENG
Soggetto:
MULTILAYER FEEDFORWARD NETWORKS; BACK-PROPAGATION; GLOBAL OPTIMIZATION; APPROXIMATION; CONVERGENCE; DERIVATIVES; UNCERTAINTY; ALGORITHM; MAPPINGS; SYSTEMS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
78
Recensione:
Indirizzi per estratti:
Citazione:
K.L. Hsu et al., "ARTIFICIAL NEURAL-NETWORK MODELING OF THE RAINFALL-RUNOFF PROCESS", Water resources research, 31(10), 1995, pp. 2517-2530

Abstract

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 andefficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. Thisstudy 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. Thenonlinear 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 (autoregressivemoving 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 ofthe watershed.

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Documento generato il 29/11/20 alle ore 10:28:41