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Pan Evaporation Simulation Based on Daily Meteorological Data Using Soft Computing Techniques and Multiple Linear Regression

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  • Anurag Malik
  • Anil Kumar

Abstract

Evaporation, a major component of hydrologic cycle, is an important parameter to many applications in water resource management, irrigation scheduling, and environmental studies. In this study, two soft computing techniques: (a) Artificial Neural Network (ANN), (b) Co-active Neuro-Fuzzy Inference System (CANFIS); and Multiple Linear Regression (MLR) were used to simulate daily pan evaporation (E p ) at Pantnagar, located at the foothills of Himalayas in the Uttarakhand state of India. Daily meteorological data such as maximum and minimum air temperature, relative humidity in the morning (7 AM) and afternoon (2 PM), wind speed, sun shine hours and pan evaporation form January 1, 2001 to December 31, 2004 were used for developing the ANN, CANFIS and MLR models. A comparison based on statistical indices such as root mean squared error (RMSE), coefficient of efficiency (CE) and correlation coefficient (r) was made among the estimated magnitudes of E p by the ANN, CANFIS and the MLR models. The architecture of ANN and CANFIS were managed by NeuroSolutions 5.0 software produced by NeuroDimension, Inc., Florida. The architecture of ANN was designed with hyperbolic tangent activation function and Delta-Bar-Delta learning algorithm and similarly the architecture of CANFIS was designed with Gaussian membership function, Takagi-Sugeno-Kang fuzzy model, hyperbolic tangent activation function and Delta-Bar-Delta learning algorithm. The results indicated that the performance of ANN model with 6-9-1 architecture in general was superior to the CANFIS and MLR models; however, the performance of CANFIS models was better than MLR models. The ANN model with all input variables and single hidden layer was found to be the best in simulating E p at Pantnagar. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:6:p:1859-1872
    DOI: 10.1007/s11269-015-0915-0
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    References listed on IDEAS

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    1. Dinpashoh, Yagob, 2006. "Study of reference crop evapotranspiration in I.R. of Iran," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 123-129, July.
    2. 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.
    3. 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.
    4. Ali-Akbar Sabziparvar & H. Tabari & A. Aeini & M. Ghafouri, 2010. "Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 909-920, March.
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    Cited by:

    1. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    2. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    3. Anurag Malik & Anil Kumar & Rajesh P. Singh, 2019. "Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3985-4006, September.

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