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Spatial mapping of pan evaporation using linear genetic programming

Published Online:pp 234-244

Daily pan evaporation is of utmost importance in planning and managing water resources. The present paper involves estimation of daily pan evaporation at a particular climatic station using daily pan evaporations of surrounding ten climatic stations covering six districts of Maharashtra state (India) with variation in elevations and weather. The surrounding stations were added one by one based on the correlation of each station with the output station. The soft computing technique of linear genetic programming was employed for this spatial mapping exercise. The models were developed for each station as output station (total 11) with the remaining stations (1 to 10) as inputs added one by one. In all 110 LGP models were developed to examine the ability of linear genetic programming to work as virtual pan as and when existing evaporimeters become inoperative. The best LGP model was for Suksale station with coefficient of correlation (r = 0.94) between observed and estimated pan evaporation. This will retrieve the missing evaporation data at one location using data at other locations.


evaporimeter, linear genetic programming, LGP, pan evaporation, spatial mapping, hydrology science


  • 1. Amiri, M.J. , Eslamian, S.S. , Abedi-Koupai, J. , Khozaei, M. (2010). ‘Estimation of daily pan evaporation using the fuzzy regression method in a semi-arid region of Iran’. Paper presented at the 10th Iranian Conference on Fuzzy Systems, 13–15 July, Shahid Beheshti University, Tehran, Iran, 300-304 Google Scholar
  • 2. Arunkumar, R. , Jothiprakash, V. (2013). ‘Reservoir evaporation prediction using data-driven techniques’. J. Hydrol. Eng. 18, 1, 40-49 Google Scholar
  • 3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a). ‘Artificial neural networks in hydrology. I: preliminary concepts’. J. Hydrol. Eng. 52, 2, 115-123 Google Scholar
  • 4. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b). ‘Artificial neural networks in hydrology. II: hydrologic applications’. J. Hydrol. Eng. 52, 2, 124-137 Google Scholar
  • 5. Banzhaf, W. , Nordin, P. , Keller, R.E. , Francone, F.D. (1998). Genetic Programming: An Introduction on the Automatic Evolution of Computer programs and its Applications. San Francisco:Morgan Kaufmann Publishers Google Scholar
  • 6. Dawson, C.W. , Wilby, R.L. (2001). ‘Hydrological modelling using artificial neural networks’. Progr. Phys. Geogr.. 25, 1, 80-108 Google Scholar
  • 7. Deschaine, L.M. (2000). ‘Tackling real-world environmental challenges with linear genetic programming’. PCAI Magazine. 15, 5, 35-37 Google Scholar
  • 8. Deschaine, L.M. , Patel, J.J. , Guthrie, R.G. , Grumski, J.T. , Ades, M.J. (2001). ‘Using linear genetic programming to develop a C/C++ simulation model of a waste incinerator’. Paper presented in The Society for Modelling and Simulation International: Advanced Simulation Technology Conference, April, Seattle, WA, 41-48 Google Scholar
  • 9. Eslamian, S.S. , Amiri, M.J. (2011). ‘Estimation of daily pan evaporation using adaptive neural-based fuzzy inference system’. International Journal of Hydrology Science and Technology. 1, 3/4, 164-175 AbstractGoogle Scholar
  • 10. Eslamian, S.S. , Gohari, A. , Biabanaki, M. , Malekian, R. (2008). ‘Estimation of monthly pan evaporation using artificial neural networks and support vector machines’. Journal of Applied Sciences. 7, 19, 2900-2903 Google Scholar
  • 11. Fritschen, L.J. (1966). ‘Energy balance method’. Paper presented at the Proceedings, American Society of Agricultural Engineers Conference on Evapotranspiration and Its Role in Water Resources Management. Chicago, IL, St. Joseph, MI, 34-37 Google Scholar
  • 12. Ghorbani, M.A. , Singh, V.P. , Daneshfaraz, R. , Kashani, M.H. (2012). ‘Modelling pan evaporation using genetic programming’. Journal of Statistics: Advances in Theory and Applications. 8, 1, 15-36 Google Scholar
  • 13. Gianniou, S.K. , Antonopoulos, V.Z. (2008). ‘Comparison of different evaporation estimation methods applied to Lake Vegoritis’. Paper presented at the Conference at Steven’s Institute of Technology, Kefalonia, Greece, PROTECTION Google Scholar
  • 14. Guitjens, J.C. (1982). ‘Models of alfalfa yield and evapotranspiration’. J. Irrig. Drain. Div. Proc. Am. Soc. Civ. Eng.. 108, IR3, 212-222 Google Scholar
  • 15. Guven, A. , Kisi, O. (2011). ‘Daily pan evaporation modeling using linear genetic programming technique’. Irrigation Sci.. 29, 2, 135-145 Google Scholar
  • 16. Harbeck, G.E. (1962). A Practical Field Technique for Measuring Reservoir Evaporation Utilizing Mass-transfer Theory. Washington, DC:US Government Printing Office , 101-105, Geological Survey Professional Paper 272-E Google Scholar
  • 17. (accessed 11 February 2013) Google Scholar
  • 18. (accessed 24 September 2013) Google Scholar
  • 19. Keskin, M.E. , Terzi, O. (2006). ‘Artificial neural network models of daily pan evaporation’. Journal of Hydrologic Engineering, ASCE. 11, 1, 65-70 Google Scholar
  • 20. Kim, S. , Kim, H.S. (2008). ‘Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modelling’. Journal of Hydrology. 351, 3–4, 299-317 Google Scholar
  • 21. Kisi, O. (2009). ‘Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks’. Hydrol. Processes. 23, 2, 213-223 Google Scholar
  • 22. Kisi, O. , Guven, A. (2010). ‘Evapotranspiration modeling using linear genetic programming technique’. Journal of Irrigation and Drainage Engineering. 136, 10, 715-723 Google Scholar
  • 23. Kohler, M.A. , Nordenson, T.J. , Fox, W.E. (1955). Evaporation from Pans and Lakes. Washington, DC:US Department of Commerce , Weather Bureau Research Paper 38 Google Scholar
  • 24. Koza, J.R. (1992). Genetic Programming on the Programming of Computers by Means of Natural Selection. Cambridge, MA:The MIT Press, A Bradford Book Google Scholar
  • 25. Maier, H.R. , Dandy, G.C. (2000). ‘Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications’. Environmental Modelling & Software. 15, 1, 101-124 Google Scholar
  • 26. Ni, Q. , Wang, L. , Zheng, B. , Sivakumar, M. (2012). ‘Evolutionary algorithm for water storage forecasting response to climate change with small data sets’. Environmental Engineering Science. 29, 8, China:The Wolonghu Wetland , 814-820 Google Scholar
  • 27. Penman, H.L. (1948). ‘Natural evaporation from open water, bare soil and grass’. Proc. R. Soc. Lond.. 193, 1032, 120-145 Google Scholar
  • 28. Solomatine, D.P. , Ostfeld, A. (2008). ‘Data-driven modelling: some past experiences and new approaches’. Journal of Hydroinformatics. 10, 1, IWA Publishing, 3-22 Google Scholar
  • 29. Sudheer, K.P. , Gosain, A.K. , Rangan, D.M. , Saheb, S.M. (2002). ‘Modeling evaporation using artificial neural network algorithm’. Hydrological Processes. 16, 16, 3189-3202 Google Scholar
  • 30. Young, A.A. (1947). ‘Evaporation from water surface in California: summary of pan records and coefficients’. Bulletin. 54, Sacramento, CA:Public Works Department , 1881-1946 Google Scholar