Estimates of Evaporation from Reservoirs Using ANN Model, a Case Study of Amir Kabir Dam, Karaj City, Iran
International Journal of Systems Science and Applied Mathematics
Volume 1, Issue 1, May 2016, Pages: 1-7
Received: Apr. 21, 2016;
Accepted: Apr. 28, 2016;
Published: May 6, 2016
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Keyvan Soltani, Nature Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Ahmad Nohegar, Faculty of Environment, University of Tehran, Tehran, Iran
Mohammad Hossein Jahangir, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Seyed Javad Sadatinejad, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Shahrzad Bouzari, Environmental Planning, Faculty of Environment, University of Tehran, Tehran, Iran
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Evaporation as a natural parameter due to the release of water from the upper part of mankind have always been of interest to scholars and researchers. In this study, we try to apply the artificial neural network model to estimate evaporation from Amir Kabir dam, the accuracy of the model is evaluated. In this context, the number 17 in the 1997 to 2014 solar years were used and consecutive errors after the procedure and the amount of evaporation from the surface of the dam structure was selected Amir Kabir The structure of the first and the second layer 7 and 8 neurons with 100 replicates to calculate it, the best results were obtained. Coefficients obtained from statistical analysis using ANN networks were considered in selecting the best structure the correlation coefficient of 90% in 0.0112 error was calculated. To determine the parameters of the evaporation rate at 17 years of data available, importing and using MATLAB software cubic best fit through the points in the data was drawn. Mann-Kendall method as well as the routing of data and trend parameters were determined the test statistics for 15 years between 1997 to 2014 solar years, and then the resulting cubic method was compared.
ANN, Evaporation, Correlation Coefficient, Mann-Kendall, Amir Kabir Dam
To cite this article
Mohammad Hossein Jahangir,
Seyed Javad Sadatinejad,
Estimates of Evaporation from Reservoirs Using ANN Model, a Case Study of Amir Kabir Dam, Karaj City, Iran, International Journal of Systems Science and Applied Mathematics.
Vol. 1, No. 1,
2016, pp. 1-7.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nourani, Vahid and Sayah fard, Mina. (2014) Analyzing the sensitivity of input data of artificial neural network for estimating daily evaporation level. 88-100.
Jahanbakhshasl, Saeed., Tadayoni, Masoumeh., Nourioughourabad, Habibeh. (2012). Analyzing changing trend of annual precipitation of Sefidrud basin using Mann-Kendall non-parametric test, Journal of Geography and regional development, No. 17.
Deswal, S., and Pal, M. (2008). “Artificial neural network based modeling of evaporation losses in reservoirs.” J. of Eng. Tech. Manag., 39, 79-83.
Ma, L., Wu, Y., Ji, J., and He, Ch. (2011). “The prediction model for soil water evaporation based on BP neural network.” Int Conf Intell Comput Tech Autom (ICICTA), 2, 276-280.
Traore, S., Wang, Y. M., and Kerh, T. (2010). “Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone.” J. of Agr. Water Manag., 97, 707-714.
Reddy, T. A. (2006). Evaluation and assessment of fault detection and diagnostic methods for centrifugal chillers-phase II, Drexel University, Philadelphia.
Zoqi, M. J., and Saeedi, M. (2011). “Modeling leachate generation using artificial neural networks.” J. of Water and Wastewater, 77, 76-84. (In Persian).
Tabesh, M., and Dini, M. (2010). “Forecasting daily urban water demand using artificial neural networks, a case study of Tehran urban water.” J. of Water and Wastewater, 73, 84-95. (In Persian).
Soltani, A. (1998). Applied regression analysis, Isfahan University Pub., Isfahan, Iran. (In Persian).
Rajaee, T., Mirbagheri, S. A., Nourani, V., and Alikhani, A. (2010). “Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model.” Int J. of Environ. Sci. Tech., 7, 93-110.
Tabari, H., Marofi, S., and Savziparvar, A. A. (2010). “Estimation of daily pan evaporation using artificial neural networks and multivariate non-linear regression.” J. of Irrig. Sci., 28, 399-406. (In Persian).