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Determination of Urban and Rural Monsoonal Evapotranspiration by Neurogenetic Models

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Impact of Climate Change on Natural Resource Management

Abstract

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.

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References

  • Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. J Water Resour Manage 19:145–161

    Article  Google Scholar 

  • 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, Indonesia

    Google Scholar 

  • 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

    Google Scholar 

  • Allison C (1992) Neural network. Sigma press, Wilmslow

    Google Scholar 

  • ASCE Task Committee (2000a) Artificial neural networks in hydrology. II: hydrological applications. J Hydrol Eng ASCE 5(2):124–137

    Article  Google Scholar 

  • ASCE Task Committee (2000b) Application of artificial neural networks in hydrology. Artificial neural networks in Hydrology I: preliminary concepts. J Hydrol Eng 5(2):115–123

    Article  Google Scholar 

  • ASCE Task Committee (2000c) Artificial neural networks in hydrology II. J. Hydrol Eng 5(2):124–132

    Article  Google Scholar 

  • Bhatt VK, Bhattacharya P, Tiwari AK (2007) Application of artificial neural network in estimation of rainfall erosivity. Hydrol J 1–2(March–June):30–39

    Google Scholar 

  • Brutsaert WH (1982) Evaporation into the atmosphere. Reidel, Dordrecht, Holland

    Book  Google Scholar 

  • Burman RD (1976) Intercontinental comparison of evaporation estimates. ASCE J Irrig Drain Eng 102:109–118

    Google Scholar 

  • Burn DH, Yulianti JS (2001) Waste-load allocation using genetic algorithms. J Water Resour Plan Manage ASCE 127(2):121–129

    Article  Google Scholar 

  • Clayton LH (1989) Prediction of class A pan evaporation in south Idaho. ASCE J Irrig Drain Eng 115(2):166–171

    Article  Google Scholar 

  • Das NG (1973) Staiistical method. Published by M. Das, 238, Manicktala Main Road (Suite no. 15) Kolkata – 54, pp 320, 483

    Google Scholar 

  • 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):67–84

    Article  Google Scholar 

  • Fahlam SE (1988) An empirical study of learning speed in back-propagation networks. Technical report cwU-CS-88- w, June

    Google Scholar 

  • 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 56:247–260

    Article  Google Scholar 

  • Grubert JP (1994) “Prediction of interfacial instabilities in estuaries using neural networks”. MSc dissertation in computer studies, University of Glamorgan, UK

    Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99

    Google Scholar 

  • Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge, MA

    Google Scholar 

  • 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, Ethiopia

    Google Scholar 

  • Jain A, Prasad Indurthy SKV (2003) Comparative analysis of event-based rainfall-runoff modeling techniques – deterministic, statistical, and artificial neural networks. J Hydrol Eng 8:93–98

    Article  Google Scholar 

  • Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE manuals and reports on engineering practices no. 70, New York

    Google Scholar 

  • 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):1–15

    Google Scholar 

  • Khan SRA (1992) Agricultural development potential of cholistan desert. N.L.C.C.H., Lahore: 127pp

    Google Scholar 

  • Khanikar PG, Nath KK (1998) Relationship of open pan evaporation rate with some important meteorological parameters. J Agri Sci Soc Northeast India 11(1):46–50

    Google Scholar 

  • Kisi O (2004) Multilayer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49(6):1025–1040

    Article  Google Scholar 

  • Kisi O (2006) Evapotranspiration estimation using feed-forward neural networks. Nord Hydrol 37(3):247–260

    Article  Google Scholar 

  • Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  • Kişi Ö, Öztürk Ö (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133(4):368–379

    Article  Google Scholar 

  • 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):219–220

    Google Scholar 

  • 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): 390–391

    Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233

    Article  Google Scholar 

  • Linacre ET (1967) Further studies of the heat transfer from a leaf. Plant Physiology 42:651–658

    Google Scholar 

  • Mahar PS, Bithin Datta (2000) Identification of pollution sources in transient groundwater systems. Water Resources Management 14(3):209–227

    Google Scholar 

  • 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):209–220

    Article  Google Scholar 

  • Malek E (2003) Microclimate of a desert playa: evaluation of annual radiation, energy, and water budgets components. Int J Climatol 23:333–345

    Article  Google Scholar 

  • Montieth JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–234

    Google Scholar 

  • Morton FI (1983) Operational estimates of areal evapo-transpiration and their significance to the science and practice of hydrology. J Hydrol 66(1–4):1–76

    Article  Google Scholar 

  • Morton FI (1969) Potential evaporation as a manifestation of regional evaporation. Water Resour Res 5(6):1244–1255

    Article  CAS  Google Scholar 

  • Naoum S, Tsanis IK (2003) Hydroinformatics in evapotranspiration estimation. Environ Modell Software 18:261–271

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282–290

    Article  Google Scholar 

  • Parasuraman K, Elshorbagy A (2007) Cluster-based hydrologic prediction using genetic algorithm-trained neural networks. J Hydrol Eng 12(ASCE):52–62

    Google Scholar 

  • Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc Roy Soc Lond Ser A Math Phys Sci 193:120–146

    Article  CAS  Google Scholar 

  • Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large scale parameters. Mon Wea Rev 100:81–92

    Article  Google Scholar 

  • 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):53–70

    Google Scholar 

  • Smith M, Allen R, Pereira L (1997) Revised FAO methodology for crop water requirements. Land and Water Development Division, FAO, Rome

    Google Scholar 

  • Stephens JC, Stewart EH (1963) A comparison of procedures for computing evaporation and evapo-transpiration, general assembly of Berkeley, 123–133, IAHS Publ No. 62

    Google Scholar 

  • Subramanya K (2005) Engineering hydrology. Tata McGraw-Hill, New Delhi, pp 58–64

    Google Scholar 

  • Sudheer KP, Gosain AK, Rangan DM, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218

    Article  Google Scholar 

  • Sudheer KP (2005) Knowledge extraction from trained neural network river flow models. J Hydrol Eng 10(4):264–269

    Article  Google Scholar 

  • Todorovic S, Stankovic M (2005) ASCE J Irrig Drain Eng 131(4):390–391

    Article  Google Scholar 

  • 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 50–55

    Google Scholar 

  • Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457

    Article  Google Scholar 

  • Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323

    Article  Google Scholar 

  • Xu C-Y*, Singh VP (1998) A review on monthly water balance models for water resources investigation and climatic impact assessment. Water Resources Management 12:31–50

    Google Scholar 

Download references

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Correspondence to Chinmoy Boral .

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Boral, C., Majumder, M., Roy, D. (2010). Determination of Urban and Rural Monsoonal Evapotranspiration by Neurogenetic Models. In: Jana, B., Majumder, M. (eds) Impact of Climate Change on Natural Resource Management. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3581-3_14

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