<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">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, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3 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 m3 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.</abstract>

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, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3 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 m3 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.

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Faculty of Civil and Environmental Engineering, Tarbiat Modares Univ., Tehran, Iran; Mailto

Shahrood University of Technology, Shahrood, Iran; Mailto:

Department of Electrical Eng., Sharif Univ. of Tech., Tehran, Iran; Mailto

Soil Conservation & Watershed Management Research Institute, Ministry of Jihad Agriculture, Tehran, Iran; Mailto

School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran; Mailto

Journal of Hydrology and Hydromechanics

In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic...

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Publié en ligne: 06 mars 2012

Pages: 16 - 32

DOI: https://doi.org/10.2478/v10098-012-0002-7

Mots clés
modelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, povodie rieky Karoon

This content is open access.

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Publié en ligne: 06 mars 2012

Pages: 16 - 32

DOI: https://doi.org/10.2478/v10098-012-0002-7

Mots clésmodelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, povodie rieky Karoon

This content is open access.

Mots clés
modelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, povodie rieky Karoon