Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
Estimation of physical variables from multichannel remotely sensed imageryusing a neural network: Application to rainfall estimation
Autore:
Hsu, KL; Gupta, HV; Gao, XG; Sorooshian, S;
Indirizzi:
Univ Arizona, Dept Hydrol & Water Resources, Tucson, AZ 85721 USA Univ Arizona Tucson AZ USA 85721 & Water Resources, Tucson, AZ 85721 USA
Titolo Testata:
WATER RESOURCES RESEARCH
fascicolo: 5, volume: 35, anno: 1999,
pagine: 1605 - 1618
SICI:
0043-1397(199905)35:5<1605:EOPVFM>2.0.ZU;2-U
Fonte:
ISI
Lingua:
ENG
Soggetto:
SNOW PARAMETERS; CLASSIFICATION; INVERSION; CLOUD; ALGORITHM;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Engineering, Computing & Technology
Citazioni:
36
Recensione:
Indirizzi per estratti:
Indirizzo: Hsu, KL Univ Arizona, Dept Hydrol & Water Resources, Bldg 11,POB 210011, Tucson, AZ Univ Arizona Bldg 11,POB 210011 Tucson AZ USA 85721 11, Tucson, AZ
Citazione:
K.L. Hsu et al., "Estimation of physical variables from multichannel remotely sensed imageryusing a neural network: Application to rainfall estimation", WATER RES R, 35(5), 1999, pp. 1605-1618

Abstract

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient atbuilding complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial andtemporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated viaan adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/11/20 alle ore 10:25:20