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Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones

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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 (Tmean), mean wind speed (Umean), sunshine duration (SD), mean relative humidity (RHmean), and extraterrestrial radiation (Ra) 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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Authors and Affiliations

  1. Department of Railroad and Civil Engineering, Dongyang University, Yeongju, Republic of Korea

    Sungwon Kim

  2. Sama Technical and Vocational Training College, Islamic Azad University, Tabriz Branch, Tabriz, Iran

    Jalal Shiri

  3. Architecture and Engineering Faculty, Civil Engineering Department, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

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  1. Sungwon Kim
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  2. Jalal Shiri
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  3. Ozgur Kisi
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Correspondence to Sungwon Kim.

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Kim, S., Shiri, J. & Kisi, O. Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones. Water Resour Manage 26, 3231–3249 (2012). https://doi.org/10.1007/s11269-012-0069-2

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  • DOI: https://doi.org/10.1007/s11269-012-0069-2

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