Artificial Neural Networks in Remote Sensing of Hydrologic Processes
Publication: Journal of Hydrologic Engineering
Volume 5, Issue 2
Abstract
Recent progress in remote sensing technologies, coupled with ongoing and planned remote sensing missions, is expected to generate hydrologic data at spatial, temporal, and spectral resolutions never previously available. Artificial neural networks (ANNs), although at early stages of hydrologic applications, are rapidly becoming an attractive tool to characterize, model, and predict complex multisource remotely sensed hydrologic data. We review and examine the utility of ANNs for hydrologic applications, with particular emphasis on remote sensing of precipitation, soil moisture, and multisource land surface data. In addition to more popularly used multilayer feedforward networks, we also review recurrent neural networks for prediction and self-organization neural networks for spatial characterization of heterogeneous land surface processes.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 5 • Issue 2 • April 2000
Pages: 138 - 144
History
Received: Aug 20, 1998
Published online: Apr 1, 2000
Published in print: Apr 2000
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