Forecasting evapotranspiration using artificial neural networks with the lowest meteorological data

Aligolinia, Tohid; Rasouli Majd, Negar; Rezaie, Hossein; Jabbari, Anahita

Document Type : Research Paper

Authors

¹ M.Sc. Student of Water Engineering (Irrigation and Drainage), Department of Water Engineering. Urmia University, Urmia, Iran

² PhD Student of Water Resources Engineering, Department of water Engineering. Urmia University, Urmia, Iran

³ Associate Professor of Water Engineering department, Urmia University

⁴ PhD Student, Water Engineering (Irrigation and Drainage), Department of Water Engineering. Urmia University, Urmia, Iran

Abstract

Accurate estimating evapotranspiration is crucial for water resource management. Evapotranspiration is an important component in water balance in different areas. Knowing the amount of water consumed per product, water engineers are able to calculate evapotranspiration as the most important component of hydrological cycle. In this study, the daily evapotranspiration of Urmia Plain was calculated using meteorological data during the period of 1984-2011 using FAO - Penman - Monteith as a base method. Then, evapotranspiration was calculated with the help of MLP and RBF neural network models using different scenarios with different input parameters. The results indicated that the daily evapotranspiration could be predicted with acceptable accuracy (RMSE = 0.985 and R2 = 0.963 for MLP network and RMSE = 0.537 and R2 = 0.963 for RBF network) using only three parameters: average temperature, sunshine hours, and wind speed. In general, it can be observed that evapotranspiration equation is more depended on the sunshine hours, wind speed, and temperature. Both MLP and RBF networks could be used for calculating the amount of evapotranspiration with high accuracy, but total accuracy of MLP network is more than RBF network.

Keywords

Main Subjects

Climatology

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Environment and Water Engineering

Forecasting evapotranspiration using artificial neural networks with the lowest meteorological data

References

References

Volume 2, Issue 2 - Serial Number 3
July 2016
Pages 122-135

Forecasting evapotranspiration using artificial neural networks with the lowest meteorological data

References

References

Volume 2, Issue 2 - Serial Number 3July 2016Pages 122-135

Volume 2, Issue 2 - Serial Number 3
July 2016
Pages 122-135