The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation
Open accessAbstract
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 at building complex nonlinear input‐output function mappings from large amounts of data. An application to high‐resolution estimation of the spatial and temporal 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 via an adaptive implementation of the MCPN method, thereby allowing on‐line improvement of the estimates.
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Cites
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
Cited by
Hong, Y., Hsu, K.-L., Sorooshian, S., & Gao, X. (2004) · Journal of Applied Meteorology · doi:10.1175/jam2173.1 · Cited by 851
Self-citationMaggioni, V., Meyers, P. C., & Robinson, M. D. (2016) · Journal of Hydrometeorology · doi:10.1175/jhm-d-15-0190.1 · Cited by 448
Component analysis of errors in satellite‐based precipitation estimates
Tian, Y., Peters‐Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G. J., Adler, R. F., Hsu, K., Turk, F. J., Garcia, M., & Zeng, J. (2009) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2009jd011949 · Cited by 431
Self-citationThe PERSIANN family of global satellite precipitation data: a review and evaluation of products
Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D., Ashouri, H., & Thorstensen, A. R. (2018) · Hydrology and Earth System Sciences · doi:10.5194/hess-22-5801-2018 · Cited by 264
Self-citationEngel, T., Fink, A. H., Knippertz, P., Pante, G., & Bliefernicht, J. (2017) · Journal of Hydrometeorology · doi:10.1175/jhm-d-16-0218.1 · Cited by 86
Metadata
- Authors
- Hsu, K., Gupta, H. V., Gao, X., & Sorooshian, S.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 1999-05
- Volume
- 35
- Issue
- 5
- Pages
- 1605-1618
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W2065832530
- Cited by
- 190
- References
- 2
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Hsu, K., Gupta, H. V., Gao, X., & Sorooshian, S. (1999). Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resources Research. https://doi.org/10.1029/1999wr900032
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