Determination of Urban and Rural Monsoonal Evapotranspiration by Neurogenetic Models

  • Chinmoy BoralEmail author
  • Mrinmoy Majumder
  • Debasri Roy


Evaporation measurement is widely used to estimate free water ­surface evaporation and is of crucial consideration in water resource development project. Evaporation is influenced by air temperature, relative humidity, wind speed, sunshine, etc. In this chapter, an attempt has been made to study the effect of the above-noted factors on reference evapotranspiration. In the present study, a Clusterized Artificial Neural Network (CANN) model was developed to estimate daily mean evapotranspiration from measured meteorological data of a tropical metro city and a rural area. The CANN model was compared with Time Series Model (TSM), Least Square Estimation Model (LSEM), and Mayer’s Method (MM) to validate the estimation. Evapotranspiration estimated by CANN model was found to yield values closest to observe ones and according to the estimation, for extreme values of the input parameters there is a difference between the outputs received for the considered two cities where the main cause for the difference was identified as rainfall.


CANN model evaporation least square estimation Mayer’s method time series 


  1. Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. J Water Resour Manage 19:145–161CrossRefGoogle Scholar
  2. Aishakh A (1998) Analysis of evaporation data as climate factors in arid regions. Water and land resources development and management for sustainable use. The tenth ICID Afro-Asian regional conference on irrigation and drainage. Denpasar, July 19–26, 1998, Bali, IndonesiaGoogle Scholar
  3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapo-transpiration guidelines for computing crop water requirements. Proceedings of FAO irrigation and drainage, Paper no. 56, Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  4. Allison C (1992) Neural network. Sigma press, WilmslowGoogle Scholar
  5. ASCE Task Committee (2000a) Artificial neural networks in hydrology. II: hydrological applications. J Hydrol Eng ASCE 5(2):124–137CrossRefGoogle Scholar
  6. ASCE Task Committee (2000b) Application of artificial neural networks in hydrology. Artificial neural networks in Hydrology I: preliminary concepts. J Hydrol Eng 5(2):115–123CrossRefGoogle Scholar
  7. ASCE Task Committee (2000c) Artificial neural networks in hydrology II. J. Hydrol Eng 5(2):124–132CrossRefGoogle Scholar
  8. Bhatt VK, Bhattacharya P, Tiwari AK (2007) Application of artificial neural network in estimation of rainfall erosivity. Hydrol J 1–2(March–June):30–39Google Scholar
  9. Brutsaert WH (1982) Evaporation into the atmosphere. Reidel, Dordrecht, HollandCrossRefGoogle Scholar
  10. Burman RD (1976) Intercontinental comparison of evaporation estimates. ASCE J Irrig Drain Eng 102:109–118Google Scholar
  11. Burn DH, Yulianti JS (2001) Waste-load allocation using genetic algorithms. J Water Resour Plan Manage ASCE 127(2):121–129CrossRefGoogle Scholar
  12. Clayton LH (1989) Prediction of class A pan evaporation in south Idaho. ASCE J Irrig Drain Eng 115(2):166–171CrossRefGoogle Scholar
  13. Das NG (1973) Staiistical method. Published by M. Das, 238, Manicktala Main Road (Suite no. 15) Kolkata – 54, pp 320, 483Google Scholar
  14. Domingo F, Villagarcía L, Brenner AJ, Puigdefábregas J (1999) Evapotranspiration model for semi-arid shrub-lands tested against data from SE Spain. J Agric Forest Meteorol 95(2):67–84CrossRefGoogle Scholar
  15. Fahlam SE (1988) An empirical study of learning speed in back-propagation networks. Technical report cwU-CS-88- w, JuneGoogle Scholar
  16. Flint AL, Childs SW (1991) Use of the Priestleye Taylor evaporation equation for soil water limited conditions in a small forest clearcut. Agric Forest Meteorol 56:247–260CrossRefGoogle Scholar
  17. Grubert JP (1994) “Prediction of interfacial instabilities in estuaries using neural networks”. MSc dissertation in computer studies, University of Glamorgan, UKGoogle Scholar
  18. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99Google Scholar
  19. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge, MAGoogle Scholar
  20. Hordofa T, Sharma A, Singh R, Dashora PK (2003) Dependence of evaporation on meteorological parameters under arid and semi-arid climatic conditions of Ethiopia. Ethiopian Agricultural Research Organization, Box 2003, Addis Ababa, EthiopiaGoogle Scholar
  21. Jain A, Prasad Indurthy SKV (2003) Comparative analysis of event-based rainfall-runoff modeling techniques – deterministic, statistical, and artificial neural networks. J Hydrol Eng 8:93–98CrossRefGoogle Scholar
  22. Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE manuals and reports on engineering practices no. 70, New YorkGoogle Scholar
  23. Keshari AK, Yadav BK (2005 (Sept–Dec)) Inflow forecasting for flat bay, Andaman and Nicobar Island using artificial neural network. Hydrol J 28(3–4):1–15Google Scholar
  24. Khan SRA (1992) Agricultural development potential of cholistan desert. N.L.C.C.H., Lahore: 127ppGoogle Scholar
  25. Khanikar PG, Nath KK (1998) Relationship of open pan evaporation rate with some important meteorological parameters. J Agri Sci Soc Northeast India 11(1):46–50Google Scholar
  26. Kisi O (2004) Multilayer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49(6):1025–1040CrossRefGoogle Scholar
  27. Kisi O (2006) Evapotranspiration estimation using feed-forward neural networks. Nord Hydrol 37(3):247–260CrossRefGoogle Scholar
  28. Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  29. Kişi Ö, Öztürk Ö (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133(4):368–379CrossRefGoogle Scholar
  30. Kisi O, Yildirim G (2005a) Discussion of “estimating actual evapotranspiration from limited climatic data using neural computing technique” by Sudheer KP, Gosain AK, and Ramasastri KS. ASCE J Irrig Drain Eng 131(2):219–220Google Scholar
  31. Kisi O, Yildirim G (2005b) Discussion of “forecasting of reference evapotranspiration by artificial neural networks” by Trajkovic S, Todorovic B, Stankovic M. ASCE J Irrig Drain Eng 131(4): 390–391Google Scholar
  32. Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233CrossRefGoogle Scholar
  33. Linacre ET (1967) Further studies of the heat transfer from a leaf. Plant Physiology 42:651–658Google Scholar
  34. Mahar PS, Bithin Datta (2000) Identification of pollution sources in transient groundwater systems. Water Resources Management 14(3):209–227Google Scholar
  35. Malek E, Bingham GE (1993) Comparison of the Bowen ratio-energy balance and the water balance methods for the measurement of evapo-transpiration. J Hydrol (Amsterdam) 146(1–4):209–220CrossRefGoogle Scholar
  36. Malek E (2003) Microclimate of a desert playa: evaluation of annual radiation, energy, and water budgets components. Int J Climatol 23:333–345CrossRefGoogle Scholar
  37. Montieth JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–234Google Scholar
  38. Morton FI (1983) Operational estimates of areal evapo-transpiration and their significance to the science and practice of hydrology. J Hydrol 66(1–4):1–76CrossRefGoogle Scholar
  39. Morton FI (1969) Potential evaporation as a manifestation of regional evaporation. Water Resour Res 5(6):1244–1255CrossRefGoogle Scholar
  40. Naoum S, Tsanis IK (2003) Hydroinformatics in evapotranspiration estimation. Environ Modell Software 18:261–271CrossRefGoogle Scholar
  41. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282–290CrossRefGoogle Scholar
  42. Parasuraman K, Elshorbagy A (2007) Cluster-based hydrologic prediction using genetic algorithm-trained neural networks. J Hydrol Eng 12(ASCE):52–62Google Scholar
  43. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc Roy Soc Lond Ser A Math Phys Sci 193:120–146CrossRefGoogle Scholar
  44. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large scale parameters. Mon Wea Rev 100:81–92CrossRefGoogle Scholar
  45. Roy P, Roy D, Mazumdar A (2004) An impact assessment of climate change and water resources availability of Damodar River basin. Hydrol J 27(3–4):53–70Google Scholar
  46. Smith M, Allen R, Pereira L (1997) Revised FAO methodology for crop water requirements. Land and Water Development Division, FAO, RomeGoogle Scholar
  47. Stephens JC, Stewart EH (1963) A comparison of procedures for computing evaporation and evapo-transpiration, general assembly of Berkeley, 123–133, IAHS Publ No. 62Google Scholar
  48. Subramanya K (2005) Engineering hydrology. Tata McGraw-Hill, New Delhi, pp 58–64Google Scholar
  49. Sudheer KP, Gosain AK, Rangan DM, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202CrossRefGoogle Scholar
  50. Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218CrossRefGoogle Scholar
  51. Sudheer KP (2005) Knowledge extraction from trained neural network river flow models. J Hydrol Eng 10(4):264–269CrossRefGoogle Scholar
  52. Todorovic S, Stankovic M (2005) ASCE J Irrig Drain Eng 131(4):390–391CrossRefGoogle Scholar
  53. Tracy JC, Marin˜o MA, Taghavi SA (1992) Predicting water demand in agricultural regions using time series forecasts of reference crop evapo-transpiration. In: Karamouz M (ed) Water resources planning and management: saving a threatened resource – in search of solutions. ASCE, New York, pp 50–55Google Scholar
  54. Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457CrossRefGoogle Scholar
  55. Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323CrossRefGoogle Scholar
  56. Xu C-Y*, Singh VP (1998) A review on monthly water balance models for water resources investigation and climatic impact assessment. Water Resources Management 12:31–50Google Scholar

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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Water Resources EngineeringJadavpur UniversityKolkataIndia
  2. 2.Regional Center, National Afforestation and Eco-development BoardJadavpur UniversityKolkataIndia

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