Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs
An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.
[1] V. P. Singh. and C. Y. Xu, "Evaluation and generalization of 13 masstransfer
equations for determining free water evaporation," Hydrological
Processes, vol. 11, pp. 311-323, 1997.
[2] O. Terz and M. E. Keskn, "Modeling of daily pan evaporation," Appled
Sc., vol. 5, pp. 368-372, 2005.
[3] H. A. R. D. Bruin, "A simple model for shallow lake evaporation,"
Applied Meterol., vol. 17, pp. 1132-1134, 1978.
[4] M. E. Anderson and H. E. Jobson, "Comparison of techniques for
estimating annual lake evaporation using climatological data," Water
Resources Res., vol. 18, pp. 630-636, 1982.
[5] R. B. Stewart and W. R. Rouse, "A simple method for determining the
evaporation from shallow lakes and ponds," Water Resources Res., vol.
12, pp. 623-627, 1976.
[6] W. Abtew, "Evaporation estimation for Lake Okeechobee in South
Florida," Irrigation and Drainage Eng., vol. 127, pp. 140-147, 2001.
[7] S. Murthy and S. Gawande, "Effect of metrological parameters on
evaporation in small reservoirs ÔÇÿAnand Sagar- Shegaon - a case study,"
J. Prudushan Nirmulan, vol. 3, no. 2, pp. 52-56, 2006.
[8] ASCE task committee on application of ANNs in Hydrology, Artificial
neural networks in hydrology, II: hydrologic applications, J. Hydraulic
Engineering, ASCE, 5 (2000) 124-137.
[9] Imrie C.E., Durucan S. and Korre A., River flow prediction using
artificial neural networks: generalisation beyond the calibration range,
Hydrol. 233 (2000), 138-153.
[10] Zealand C.M., Burn D.H. and Simonovic S.P., Short term streamflow
forecasting using artificial neural networks, Hydrol. 214 (1999), 32-48.
[11] Sudheer K.P., Gosain A.K., Mohan R.D. and Saheb S.M., Modelling
evaporation using an artificial neural network algorithm, Hydrological
Processes 16 (2002), 3189-3202.
[12] Braddock R.D., Kremmer M.L. and Sanzogni L., Feed-forward artificial
neural network model for forecasting rainfall run-off, Environmetrics 9
(1998), 419-432.
[13] Dibike Y.B. and Solomatine D.P., River flow forecasting using artificial
neural networks, Phys. Chem. Earth (B) 26 (2001), 1-7.
[14] Bishop C. M., Neural networks for pattern recognition, Oxford:
Clarendon Press, 1995.
[15] Rumelhart D.E., Hinton G.E. and Williams R.J., Learning internal
representation by error propagation, Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, Volume 1: Foundations
(ed.), Cambridge, MA: The MIT Press, 1996, pp. 318-362.
[1] V. P. Singh. and C. Y. Xu, "Evaluation and generalization of 13 masstransfer
equations for determining free water evaporation," Hydrological
Processes, vol. 11, pp. 311-323, 1997.
[2] O. Terz and M. E. Keskn, "Modeling of daily pan evaporation," Appled
Sc., vol. 5, pp. 368-372, 2005.
[3] H. A. R. D. Bruin, "A simple model for shallow lake evaporation,"
Applied Meterol., vol. 17, pp. 1132-1134, 1978.
[4] M. E. Anderson and H. E. Jobson, "Comparison of techniques for
estimating annual lake evaporation using climatological data," Water
Resources Res., vol. 18, pp. 630-636, 1982.
[5] R. B. Stewart and W. R. Rouse, "A simple method for determining the
evaporation from shallow lakes and ponds," Water Resources Res., vol.
12, pp. 623-627, 1976.
[6] W. Abtew, "Evaporation estimation for Lake Okeechobee in South
Florida," Irrigation and Drainage Eng., vol. 127, pp. 140-147, 2001.
[7] S. Murthy and S. Gawande, "Effect of metrological parameters on
evaporation in small reservoirs ÔÇÿAnand Sagar- Shegaon - a case study,"
J. Prudushan Nirmulan, vol. 3, no. 2, pp. 52-56, 2006.
[8] ASCE task committee on application of ANNs in Hydrology, Artificial
neural networks in hydrology, II: hydrologic applications, J. Hydraulic
Engineering, ASCE, 5 (2000) 124-137.
[9] Imrie C.E., Durucan S. and Korre A., River flow prediction using
artificial neural networks: generalisation beyond the calibration range,
Hydrol. 233 (2000), 138-153.
[10] Zealand C.M., Burn D.H. and Simonovic S.P., Short term streamflow
forecasting using artificial neural networks, Hydrol. 214 (1999), 32-48.
[11] Sudheer K.P., Gosain A.K., Mohan R.D. and Saheb S.M., Modelling
evaporation using an artificial neural network algorithm, Hydrological
Processes 16 (2002), 3189-3202.
[12] Braddock R.D., Kremmer M.L. and Sanzogni L., Feed-forward artificial
neural network model for forecasting rainfall run-off, Environmetrics 9
(1998), 419-432.
[13] Dibike Y.B. and Solomatine D.P., River flow forecasting using artificial
neural networks, Phys. Chem. Earth (B) 26 (2001), 1-7.
[14] Bishop C. M., Neural networks for pattern recognition, Oxford:
Clarendon Press, 1995.
[15] Rumelhart D.E., Hinton G.E. and Williams R.J., Learning internal
representation by error propagation, Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, Volume 1: Foundations
(ed.), Cambridge, MA: The MIT Press, 1996, pp. 318-362.
@article{"International Journal of Earth, Energy and Environmental Sciences:51174", author = "Surinder Deswal and Mahesh Pal", title = "Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs", abstract = "An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.", keywords = "Artificial neural network, evaporation losses,
multiple linear regression, modeling.", volume = "2", number = "3", pages = "18-5", }