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Titolo: ARTIFICIAL NEURALNETWORK MODELING OF THE RAINFALLRUNOFF 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:
 00431397(1995)31:10<2517:ANMOTR>2.0.ZU;2D
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 MULTILAYER FEEDFORWARD NETWORKS; BACKPROPAGATION; 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 NEURALNETWORK MODELING OF THE RAINFALLRUNOFF PROCESS", Water resources research, 31(10), 1995, pp. 25172530
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 threelayer 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 rainfallrunoff relationship of the mediumsize Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressivemoving average with exogenous inputs) time series approach or the conceptual SACSMA (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 inputoutput 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