Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs

Authors: Surinder Deswal, Mahesh Pal

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056338

References:

[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.