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A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models

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  • Paresh Shirsath
  • Anil Singh

Abstract

Evaporation estimates are needed for efficient management of water resources at a farm scale as well as at a regional or catchment scale. This paper presents application of artificial neural networks (ANN), statistical regression and climate based models viz.: Penman, Priestley–Taylor and Stephens and Stewart, for estimation of daily pan evaporation. Six different measured weather variables comprising various combinations of maximum and minimum air temperature, sun shine hours, wind speed, relative humidity I and II were used. Randomly selected 1,096 daily records were used to develop the models of ANN and regression, and 365 daily records were used as independent data set for performance evaluation, which was not used previously in any of the model development process. The results of the developed ANN and multiple linear regression (MLR) models along with Penman, Priestley-Taylor and Stephens and Stewart models were compared statistically with observed pan evaporation values. Comparison showed that there is slightly better agreement between the ANN estimations and measurements of daily pan evaporation than other models. Copyright Springer Science+Business Media B.V. 2010

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  • 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.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:8:p:1571-1581
    DOI: 10.1007/s11269-009-9514-2
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    1. 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.
    2. S. Mohan & N. Ramsundram, 2016. "Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3315-3330, August.
    3. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.
    4. 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.
    5. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    6. 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.
    7. Ignacio Lorite & Margarita García-Vila & María-Ascensión Carmona & Cristina Santos & María-Auxiliadora Soriano, 2012. "Assessment of the Irrigation Advisory Services’ Recommendations and Farmers’ Irrigation Management: A Case Study in Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2397-2419, June.
    8. Anurag Malik & Anil Kumar, 2015. "Pan Evaporation Simulation Based on Daily Meteorological Data Using Soft Computing Techniques and Multiple Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1859-1872, April.
    9. Safar Marofi & Hossein Tabari & Hamid Abyaneh, 2011. "Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1417-1435, March.
    10. Salvatore Campisi-Pinto & Jan Adamowski & Gideon Oron, 2012. "Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3539-3558, September.

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