Evapotranspiration and evaporation measurements are important parameters for many agricultural activities such as water resource management and environmental studies. There are several models which can determine pan coefficient (K Pan ), using wind speed, relative humidity and fetch length conditions. This paper analyses seven exiting pan models to estimate K Pan values for two different climates of Iran. Monthly mean reference crop evapotranspiration (ET 0 ) was calculated according to the pan-ET 0 model. The results showed that estimated pan coefficients by majority of the suggested models were not statistically accurate to be used in the pan-ET 0 conversion method. However, for the cold semi-arid climate condition, the best K Pan models for estimation of ET 0 were Orang and Raghuwanshi-Wallender, respectively. Also, the Snyder and Orang models were best fitted models for warm arid climate, respectively. The mean annual value of K Pan , determined by PenmanMonteith FAO 56 (PMF-56) standard model for warm arid sites, was approximately 32% higher than the corresponding value in the cold semi-arid climate. Similarly, the mean annual ET 0 in the warm arid sites was 66% higher, compared to the ET 0 of the cold semi-arid sites. These types of warm arid and semi-arid climates are found widely throughout the world. A.A. Sabziparvar et al.

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In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R 2 , MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m 3 s -1 , 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m 3 s -1 , 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling. Cieľom štúdie bolo porovnať možnosti dlhodobej simulácie denných prietokov v rieke Karoon pomocou novovyvinutej fuzzy metódy aktívneho učenia (Active Learning Method -ALM) a známej metódy vektormi podporených strojov (Support Vector Machine -SVM), optimalizovanej genetickým algoritmom (GA). Na tréning a testovanie modelov boli použité časové rady denných prietokov za obdobie rokov 1991 až 1996 a 1996 až 1999. Hodnoty parametrov Nash-Sutcliffe, Bias, R 2 , MPAE a PTVE pre model ALM boli 0,81; 5,5 m 3 s -1 ; 0,81; 12,9% a 1,9%. Parametre v tom istom poradí pre model SVM boli 0,8 -10,7 m 3 s -1 , 0,81; 7,3%; a -3,6%. Z výsledkov simulácií vyplýva, že aplikáciou metód ALM a SVM možno získať porovnateľné a akceptovateľné výsledky. Podobnosť výsledkov medzi ALM a SVM implikuje vhodnosť novovyvinutej metódy ALM pre simuláciu odtoku. Tréning ALM je ľahší a jednoduchší ako je tréning ďalších dátami riadených modelov podobného typu. Navyše algoritmus ALM je schopný identifikovať a zoradiť efektívne vstupné premenné pre modelovanie odtoku. Na základe dosiahnutých výsledkov možno metódu ALM zaradiť medzi nové, alternatívne metódy modelovania odtoku.KĽÚČOVÉ SLOVÁ: modelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, povodie rieky Karoon.Brought to you by | MIT Libraries Authenticated Download Date | 5/10/18 10:26 AM Comparison between active learning method and support vector machine for runoff modeling

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