Please use this identifier to cite or link to this item:
http://117.252.14.250:8080/jspui/handle/123456789/3741
Title: | Theme 1-6-Neural computation technique for estimating of hydrologic cycle |
Authors: | Sudheer, K. P. Gosain, A. K. Ramasastri, K. S. |
Keywords: | Artificial neural networks (ANN) Estimate the values of crop evapotranspiration(ET) |
Issue Date: | 2000 |
Publisher: | National Institute of Hydrology |
Abstract: | An artificial neural network (ANN) methodology was employed to estimate the values of crop evapotranspiration (ET) and evaporation from daily values of air temperature. The study investigated two ANN architectures, a radial basis function network and a multi layer feed forward back propagation network for mapping an input output relationship. The ANN models compared favorably with observed values of the processes. At the same time, it represents an improvement upon the prediction accuracy and flexibility over current methods. A statistical analysis of the results suggested that the ANN model could be employed in estimating ET and evaporation using limited weather data with reasonable accuracy. |
URI: | http://117.252.14.250:8080/jspui/handle/123456789/3741 |
Appears in Collections: | ICIWRM-2000, Proceedings of International Conference on Integrated Water Resources Management for Sustainable Development, 19-21 December, 2000, New Delhi, India Vol.-I |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Theme 1-6-Neural computation technique for estimating of hydrologic cycle.pdf | 90.71 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.