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Determination of Evapotranspiration from Stream Flow with the Help of Classified Neurogenetic Model

  • Mrinmoy MajumderEmail author
  • Pankaj Roy
  • Asis Mazumdar
Chapter

Abstract

The efficiency of an adaptive neuro-fuzzy computing technique in estimation of reference evapotranspiration (ET0) from limited stream flow is investigated in this chapter. Forty years of stream flow data are clustered into nine groups, with the help of 11 basic rules where a ranking mechanism is used to sort the stream flows, and from these sorted data, top 10 stream flows and the same for that year are applied to the model as input. Corresponding ET0, derived from Penman equation, is also classified into nine groups with the help of the same 11 basic rules and applied as output of the model. The stream flow dataset predicted by an HECHMS model is also clustered and applied into the same model to verify model flexibility and efficiency. Correct classification rate, coefficient of relationship, standard deviation, and coefficient of efficiency are used as the measure of the applicability of neural model in predicting ET0 from stream flow. These are compared with the ET0 derived from the Penman model. The comparison results reveal that the neuro-fuzzy models could be employed successfully in modeling ET0 from stream flow, if the datasets of both ET0 and stream flow are clustered according to the basic rules developed as per the stability of the catchment.

Keywords

Classified neurogenetic models evapotranspiration reverse hydrology river Damodar stream flow 

Notes

Acknowledgment

Authors will like to thank Damodar Valley Corporation for providing the necessary data base which are used in the study and Er. Chandan Ray, Former Chief Engineer, Irrigation and Waterways Department, West Bengal, India for his valued scientific advice.

References

  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. 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, Rome, 1998Google Scholar
  3. ASCE Task Committee (2000a) Application of artificial neural networks in hydrology. Artificial neural networks in hydrology I: preliminary concepts. J Hydrol Eng 5(2):115–123CrossRefGoogle Scholar
  4. ASCE Task Committee (2000b) Artificial neural networks in hydrology II. J Hydrol Engg 5(2):124–132CrossRefGoogle Scholar
  5. 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
  6. Brutsaert WH (1982) Evaporation into the atmosphere: theory, history and applications. Springer, Boston, p 299CrossRefGoogle Scholar
  7. Burn DH, Yulianti JS (2001) Waste-load allocation using genetic algorithms. J Water Resour Plan Manage ASCE 127(2):121–129CrossRefGoogle Scholar
  8. Domingo F, Sanchez G, Moro MJ, Brenner AJ, Puigdefabregas J (1998): Measurement and modelling of rainfall interception by three semi-arid canopies. Agric For Meteor 91:275–292CrossRefGoogle Scholar
  9. Fahlam SE (1988) An empirical study of learning speed in back-propagation networks. Technical Report cwU-CS-88-w, June 1988Google Scholar
  10. 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
  11. Hargreaves GH, Samani ZA (1985) Reference crop evapo-transpiration from temperature. Appl Eng Agric 1(2):96–99Google Scholar
  12. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge, MAGoogle Scholar
  13. Jain A, Prasad Indurthy SKV (2003) Comparative analysis of event-based rainfall-runoff modeling techniques – deterministic, statistical, and artificial neural networks, March/April. J Hydrol Eng 8:93–98CrossRefGoogle Scholar
  14. Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE manuals and reports on engineering practices no. 70, New YorkGoogle Scholar
  15. 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
  16. Kisi O (2006) Evapotranspiration estimation using feed-forward neural networks. Nord Hydrol 37(3):247–260CrossRefGoogle Scholar
  17. Kis,i Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  18. 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
  19. Majumder M, Barman RN, Roy P, Jana BK, Mazumdar A (2009) Application of neuro-genetic algorithm to determine reservoir response in different hydrologic adversaries. J Soil Water Resour 4(1):17–27Google Scholar
  20. 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
  21. Malek E (2003) Microclimate of a desert playa: evaluation of annual radiation, energy, and water budgets components. Int J Climatol 23:333–345CrossRefGoogle Scholar
  22. Montieth JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–234Google Scholar
  23. 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
  24. Naoum S, Tsanis IK (2003) Hydroinformatics in evapotranspiration estimation. Environ Modell Software 18:261–271CrossRefGoogle Scholar
  25. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282–290CrossRefGoogle Scholar
  26. Parasuraman K, Elshorbagy A (2007) Cluster-based hydrologic prediction using genetic algorithm-trained neural networks. J Hydrol Eng 12:52–62CrossRefGoogle Scholar
  27. Penman HL (1948) Natural evaporation from open water bare soil and grass, proceedings of the Royal Society of London, Series A. Math Phys Sci 193:120–146CrossRefGoogle Scholar
  28. 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
  29. 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
  30. Smith M, Allen R, Pereira L (1997) Revised FAO methodology for crop water requirements. Land and Water Development Division, FAO, RomeGoogle Scholar
  31. Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapo-transpiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218CrossRefGoogle Scholar
  32. Sudheer KP (2005) Knowledge extraction from trained neural network river flow models. J Hydrol Eng 10(4):264–269CrossRefGoogle Scholar
  33. Tracy JC, Marinõ 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
  34. Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457CrossRefGoogle Scholar
  35. Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323CrossRefGoogle Scholar
  36. US Army Corps of Engineers, Hydrologic Engineering Center (HEC) (2000) Hydrologic Modeling System, HECHMS: Technical Reference Manual. CPD-74B. US Army Corps of Engineers, Hydrologic Engineering Center, Davis, CA, http://www.hec.usace.army.mil/software/hec-hms/documentation/hms_technical.pdf, 2000
  37. Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour Res 27(9):2467–2471CrossRefGoogle Scholar
  38. Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plann Manage 125(1):25–33CrossRefGoogle Scholar

Copyright information

© 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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