Used from Entropy theory and Gamma test in the determination input variables for daily evaporation estimation

Document Type : Research Article


1 water engineering department, Tabriz university, master student

2 water engineering department, Tabriz university,

3 tabriz university


This research assessment ability of entropy theory and Gamma test for input variable of Artificial Network and Support Vector Machine as evaporation estimation for Rasht, Astara and Anzali in Guilan province. According the results, for Rasht synoptic, Astara and Anzali, Entropy Theory is determined that existence all variables are effective for modelling. Gamma test, for Rasht station two variables contain maximum and average humidity, for Anzali station three variables, contain minimum and average temperature and average humidity and for Astara station one variable contain wind speed eliminated form optimal composition. According to results two model’s Artificial Neural Network and Support Vector Machine performance have been acceptable. For determination of single input data in Rasht station Entropy Theory method and in the Anzali station Gamma test method have had good performance. For Astara station both of them have had good performance. Generally, according to the results, it can be said that Entropy theory had the stronger performance than Gamma test. But at the look of managerial, because the a few input variable selection than the Entropy theory, the Gamma performance has been acceptable than Entropy theory.


Main Subjects

[1]. Ejlali F, Weather and climatology. Iran: Payamnoor University Press; 2004. (In Persian)
[2]. Nourani V, Baghanam AH, Adamowski J, Kisi O. Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology. 2014; 514:358-77.
[3]. Ahmadi A, Han D, Karamouz M, Remesan R. Input data selection for solar radiation estimation. Hydrological processes. 2009; 23(19):2754-64.
[4]. Moghaddamnia A, Gousheh MG, Piri J, Amin S, Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources. 2009; 32(1):88-97.
 [5]. Sharifi A, Dinpashoh Y, Mirabbasi R. Daily runoff prediction using the linear and non-linear models. Water Science and Technology. 2017:wst2017234.
[6]. Seefi A, Mirlatifi M, Reahi H. Introduction and application of Least Square Support Vector Machine (LSSVM) for simlulation of reference evaporation and uncertainty analysis of results, a case study fo the Kerman city.Irrigation & Water Engineering. 2013; 13 (5):67-78. (In Persian)
[7]. Kim S, Shiri J, Kisi O, Singh VP. Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water resources management. 2013; 27(7):2267-86.
[8]. Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A. Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications. 2014; 41(11):5267-76.
[9]. Tezel G, Buyukyildiz M. Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and applied climatology. 2016; 124(1-2):69-80.
[10]. Kisi O. Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology. 2015; 528:312-20.
[11]. Kisi O, Genc O, Dinc S, Zounemat-Kermani M. Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Computers and Electronics in Agriculture. 2016; 122:112-7.
[12]. Keshtegar B, Piri J, Kisi O. A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Computers and Electronics in Agriculture. 2016; 127:120-30.
[13]. Sharifi A.R, Dinpashih Y, Fahkerifard A, Moghadamnia, AR. Optimum combination of variables for runoff simulation in Amameh Watershed using Gamma Test, Journal of Soil and Water. 2013; 23(3):72-59. (In Persian)
[14]. Durrant PJ. winGamma: A non-linear data analysis and modelling tool with applications to flood prediction. Unpublished PhD thesis, Department of Computer Science, Cardiff University, Wales, UK. 2001 Jun 25.
[15]. Evans D, Jones AJ. A proof of the Gamma test. InProceedings of the Royal Society of London a: Mathematical, Physical and Engineering Sciences 2002 Nov 8 (Vol. 458, No. 2027, pp. 2759-2799). The Royal Society.
[16]. Shannon C. E, & Weaver W, Urban:University of Illinois Press; 1949.
[17]. Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL. Flood estimation at ungauged sites using artificial neural networks. Journal of hydrology. 2006; 319(1):391-409.
[18]. Harmancioglu NB, Alpaslan N. WATER QUALITY MONITORING NETWORK DESIGN: A PROBLEM OF MULTI‐OBJECTIVE DECISION MAKING. JAWRA Journal of the American Water Resources Association. 1992; 28(1):179-92.
[19]. Coulibaly P, Anctil F, Bobee B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology. 2000; 230(3):244-57.
[20]. ASCE Task Committee. Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering. 2000; 5(2):115-23.
[21]. Kavzoglu T, Colkesen I. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation. 2009; 11(5):352-9.
[22]. Nash J.E, Sutcliffe I.V. River flow forecasting through conceptual models, Part I, A discussion of principles, Journal of Hydrology. 1970; 10(2):282-290.
Volume 5, Issue 2
July 2018
Pages 535-549
  • Receive Date: 23 August 2017
  • Revise Date: 22 January 2018
  • Accept Date: 04 February 2018
  • First Publish Date: 22 June 2018