Comparison between active learning method and support vector machine for runoff modeling

Title:

Comparison between active learning method and support vector machine for runoff modeling
Porovnanie metódy aktívneho učenia s metódou vektormi podporených strojov pri modelovaní odtoku

Creator:

Shahraiyni, Hamid Taheri, Ghafouri, Mohammad Reza, Shouraki, Saeed Bagheri, Saghafian, Bahram, and Nasseri, Mohsen

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c5795647-83a5-4f1d-a380-ea1273a2c00f
uuid:c5795647-83a5-4f1d-a380-ea1273a2c00f
doi:10.2478/v10098-012-0002-7

Subject:

runoff modeling, active learning method (ALM), support vector machine (SVM), fuzzy modeling, genetic algorithm, Karoon River basin, modelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, and povodie rieky Karoon

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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. and 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, R2 , MPAE a PTVE pre model ALM boli 0,81; 5,5 m3 s-1; 0,81; 12,9% a 1,9%. Parametre v tom istom poradí pre model SVM boli 0,8 -10,7 m3 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.

Language:

Slovak

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

16-32

Source:

Journal of hydrology and hydromechanics | 2012 Volume:60 | Number:1

Harvested from:

CDK

Metadata only:

false