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Water Resources Management

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01-09-2012

Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones

Authors: Sungwon Kim, Jalal Shiri, Ozgur Kisi

Published in: Water Resources Management | Issue 11/2012

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Abstract

The purpose of this study was to develop and apply the neural networks models to estimate daily pan evaporation (PE) for different climatic zones such as temperate and arid climatic zones, Republic of Korea and Iran. Three kinds of the neural networks models, namely multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and support vector machine-neural networks model (SVM-NNM), were used to estimate daily PE. The available climatic variables, consisted of mean air temperature (T_mean), mean wind speed (U_mean), sunshine duration (SD), mean relative humidity (RH_mean), and extraterrestrial radiation (R_a) were used to estimate daily PE using the various input combinations of climate variables. The measurements for the period of January 1985–December 1990 (Republic of Korea) and January 2002–December 2008 (Iran) were used for training and testing the employed neural networks models. The results obtained by SVM-NNM indicated that it performs better than MLP-NNM and GRNNM for estimating daily PE. A comparison was also made among the employed models, which demonstrated the superiority of MLP-NNM, GRNNM, and SVM-NNM over Linacre model and multiple linear regression model (MLRM).

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Title: Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones
Authors: Sungwon Kim
Jalal Shiri
Ozgur Kisi
Publication date: 01-09-2012
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 11/2012
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-012-0069-2

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