Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Jain, S. K., Nayak, P. C., & Sudheer, K. P. Hydrological Processes, 22(13):2225–2234, 2008. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.6819![Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Estimation of evapotranspiration (ET) requires a knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although ET is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. The major objective of this study was to estimate ET using an artificial neural network (ANN) technique and to examine if a trained neural network with limited input variables can estimate ET efficiently. The results indicate that even with limited climatic variables an ANN can estimate ET accurately. The paper also outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. Such an analysis performed on the ANN-ET models developed was able to explain the reasons for the ANN's potential in estimating the ET effectively from limited climatic data. Copyright © 2008 John Wiley & Sons, Ltd.
@article{jain_models_2008,
title = {Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation},
volume = {22},
copyright = {Copyright © 2008 John Wiley \& Sons, Ltd.},
issn = {1099-1085},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.6819},
doi = {10.1002/hyp.6819},
abstract = {Estimation of evapotranspiration (ET) requires a knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although ET is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. The major objective of this study was to estimate ET using an artificial neural network (ANN) technique and to examine if a trained neural network with limited input variables can estimate ET efficiently. The results indicate that even with limited climatic variables an ANN can estimate ET accurately. The paper also outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. Such an analysis performed on the ANN-ET models developed was able to explain the reasons for the ANN's potential in estimating the ET effectively from limited climatic data. Copyright © 2008 John Wiley \& Sons, Ltd.},
language = {fr},
number = {13},
urldate = {2020-06-24},
journal = {Hydrological Processes},
author = {Jain, S. K. and Nayak, P. C. and Sudheer, K. P.},
year = {2008},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.6819},
keywords = {Penman Monteith, artificial neural network, evapotranspiration, radiation, temperature},
pages = {2225--2234},
}
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