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Evaporation Estimation from Meteorological Parameters Using Multiple Linear Regression Model


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1 Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
     

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Evaporation is one of the main elements affecting water storage and temperature in the hydrological cycle and it plays an important role in evaluation of water availability. Considering the difficulty involved in direct method of evaporation estimation and limitation of empirical methods, an attempt has been made to estimate evaporation by multiple linear regression with the aid of gamma test (GT). The data of meteorological parameters viz., average temperature (Tavg), wind speed (W), average relative humidity (Rhavg) and sunshine hours (S) were used as input parameters and evaporation was considered as output parameter. The performance of developed model was evaluated in terms of mean squared error (MSE) and correlation co-efficient (r). In developed model, MSE was found to be 1.13 and 0.92 in training and testing phase, respectively. The model demonstrated good values of correlation co-efficient, respectively as 0.91 and 0.95 for training and testing period indicating the suitability of model to estimate the evaporation.

Keywords

Evaporation, Meteorological Parameters, MLR, Gamma Test.
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  • Abramson, I.S. (1982). On bandwidth variation in kernel estimates-a square ischolar_main law. Ann. Statist., 10 : 1217–1223. MR0673656
  • Abtew, W. (2001). Evaporation estimation for Lake Okeechobee in South Florida. Irrigation & Drainage Engg., 127 : 140-147.
  • Agalbjorn, S., Koncar, N. and Jones, A.J. (1997). A note on the gamma test.Neural Comput. Appl., 5(3) : 131–133. ISSN 0-941-0643.
  • Almedeij, J. (2012). Modeling pan evaporation for Kuwait by multiple linear regression.The Scientific World J., 2012 (article id 574742), pp. 1-9.
  • Alshaikh, A. (1998). Analysis of evaporation data as affected by climatic factors in arid regions. Water and land resources development and management for sustainable use. Vol. II-A. The tenth ICID Afro-Asian regional conference on irrigation and drainage, Denpasar, Bali, Indonesia, 19-26 July 1998. A-27, 9 –10.
  • Anderson, M.E. and Jobson, H.E. (1982). Comparison of techniques for estimating annual lake evaporation using climatological data.Water Resour. Res., 18 : 630-636.
  • Bruin, H.D. (1978). A simple model for shallow lake evaporation. Applied Meteorol., 17 : 1132-1134.
  • Cahoon, E.J., Costello, T.A. and Ferguson, J.A. (1991). Estimating pan evaporation using limited meteorological observations. Agric. & Forestry Meteorol., 55 :181-190.
  • Chandra, A., Shrikhande, V.J. and Kulshreshta, R. (1988). Relationship of pan evaporation with meteorological parameters. J. Indian Water Reso. Soc., 8(2) : 41- 44.
  • Chiu, C.C., Cook, D.F. and Pignatiello, J.J. (1995). Radial basis function neural network for Kraft pulping forecasting. Internat. J. Ind. Eng., 2 (3) : 209–215.
  • Cook, D.F. and Chiu, C.C. (1997). Predicting the internal bond strength of particleboard utilizing a radial basis function neural network. Engg. Appl. AI., 10(2) : 171–177.
  • Diggle, P.J. and Hutchinson, M.F. (1989). On spline smoothing with auto-correlated errors. Australian J. Statist., 31 :166–182.
  • Fan, J. and Gijbels, I. (1996). Local polynomial modeling and its applications. Chapman & Hall, London. Great lakes buoy and associated synoptic weather patterns. J. Hydro Meteorol., 3(1) : 3-12.
  • Fennessey, N.M. and Vogel, R.M. (1996). Regional models of potential evaporation and reference evapotranspiration for the northeast USA. J. Hydrol., 184 : 337-354.
  • Gao, X.M., Gao, X.Z., Tanskanen, J. and Ovaska, S.J. (1997). Power prediction in mobile communications systems using an optimal neural network structure. IEEE Trans. Neural Networks, 8(6) : 1446–1455.
  • Hardle, W. (1990). Applied nonparametric regression. Cambridge University Press, New York.
  • Hastle, T. and Loader, C. (1993). Local regression: Automatic kernel carpentry (with discussion). Statist. Sci., 8 : 120–143.
  • Hoskins, J.C. and Himmelblau, D.M. (1988). Artificial neural networks models of knowledge representation in chemical engineering. Comput. Chemi. Engg., 12(9) : 881–890.
  • Jones, M.C., Linton, O. and Nielsen, J.P. (1995). A simple bias reduction method for density estimation. Biometrika, 82(2) : 327–338.
  • Koncar, N. (1997). Optimisation methodologies for direct inverse neurocontrol. Ph.D. Thesis, Department of computing, Imperial College of Science, Technology and Medicine, University of London.
  • Murthy, S. and Gawande, S. (2006). Effect of meteorological parameters on evaporation in small reservoirs ‘Anand Sagar’ Shegaon – a case study. J. Prudushan Nirmulan, 3(2) : 52-56.
  • Nottingham, Q.J. (1995). Model-robust quantile regression. Ph.D. Dissertation, Virginia.
  • Robinson, P.M. (1983). Nonparametric estimators for time series. J. Time Series Anal., 4 :185–207.
  • Samiuddin, M. and El-Sayyad, G. M. (1990). On nonparametric kernel density estimates.Biometrika, 77 : 865–874. MR1086696
  • Senturk, K. and Kocyigit, F.O. (2010). A case study: Evaporation estimation at Oymapinar Dam. Proc., BALWOIS 2010, Balkan institute for water and environment, Montpellier, France, 1–7.
  • Shrivastava, S.K., Misra, S.K., Sahu, A.K. and Bose, D. (2000). Correlation between pan evaporation and climatic parameters for Sunderbans- a case study. IE(I) Journal – AG. 81, 55-58.
  • Stewart, R.B. and Rouse, W.R. (1976). A simple method for determining the evaporation from shallow lakes and ponds. Water Resour. Res., 12 : 623-627.
  • Tjostheim, D. (1994). Nonlinear time series: a selective review. Scandinavian J. Statist., 21 : 97–130.
  • Tsui, A.P.M., Jones, A.J. and Oliveira, A.G. (2002). The construction of smooth models usingirregular embeddings determined by a gamma test analysis. Neural Comput Appl., 10(4) : 318–329. doi:10.1007/s005210200004
  • Wand, M.P. and Jones, M.C. (1995). Kernel smoothing, Chapman and Hall, London. MR1319818.
  • Yakowitz, S. (1987). Nearest-neighbor methods for time series analysis. J. Time Series Anal., 8 : 235–247.

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  • Evaporation Estimation from Meteorological Parameters Using Multiple Linear Regression Model

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Authors

Neeraj Kumar
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Gaurang Joshi
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Rohit Kumar
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Pankaj Kumar
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India

Abstract


Evaporation is one of the main elements affecting water storage and temperature in the hydrological cycle and it plays an important role in evaluation of water availability. Considering the difficulty involved in direct method of evaporation estimation and limitation of empirical methods, an attempt has been made to estimate evaporation by multiple linear regression with the aid of gamma test (GT). The data of meteorological parameters viz., average temperature (Tavg), wind speed (W), average relative humidity (Rhavg) and sunshine hours (S) were used as input parameters and evaporation was considered as output parameter. The performance of developed model was evaluated in terms of mean squared error (MSE) and correlation co-efficient (r). In developed model, MSE was found to be 1.13 and 0.92 in training and testing phase, respectively. The model demonstrated good values of correlation co-efficient, respectively as 0.91 and 0.95 for training and testing period indicating the suitability of model to estimate the evaporation.

Keywords


Evaporation, Meteorological Parameters, MLR, Gamma Test.

References