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Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones

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  • Sungwon Kim
  • Jalal Shiri
  • Ozgur Kisi

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). Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Sungwon Kim & Jalal Shiri & Ozgur Kisi, 2012. "Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3231-3249, September.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:11:p:3231-3249
    DOI: 10.1007/s11269-012-0069-2
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    References listed on IDEAS

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    1. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    2. Seema Chauhan & R. Shrivastava, 2009. "Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 825-837, March.
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    1. Miao Zhang & Bo Su & Majid Nazeer & Muhammad Bilal & Pengcheng Qi & Ge Han, 2020. "Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China," Land, MDPI, vol. 9(7), pages 1-14, July.
    2. Yanhu He & Kairong Lin & Xiaohong Chen & Changqing Ye & Lei Cheng, 2015. "Classification-Based Spatiotemporal Variations of Pan Evaporation Across the Guangdong Province, South China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 901-912, February.
    3. Sungwon Kim & Vijay Singh & Youngmin Seo & Hung Kim, 2014. "Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 185-206, January.
    4. Wang, Hong & Sun, Fubao & Liu, Fa & Wang, Tingting & Liu, Wenbin & Feng, Yao, 2023. "Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China," Agricultural Water Management, Elsevier, vol. 287(C).
    5. Sungwon Kim & Jalal Shiri & Ozgur Kisi & Vijay Singh, 2013. "Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2267-2286, May.
    6. Youngmin Seo & Sungwon Kim & Vijay Singh, 2015. "Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2189-2204, May.
    7. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    8. Ezekiel I. D.* & Alabi N. O., 2018. "Boosted Regression Tree for Modeling Evaporation Piche Using Other Climatic Factors Over Ilorin," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 4(9), pages 98-106, 09-2018.
    9. Ozgur Kisi & Taner Cengiz, 2013. "Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3541-3553, August.
    10. Yeşim Ahi & Çiğdem Coşkun Dilcan & Daniyal Durmuş Köksal & Hüseyin Tevfik Gültaş, 2023. "Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2607-2624, May.

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