Multilayer perceptron with different training algorithms for streamflow forecasting View Full Text


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Article Info

DATE

2014-03

AUTHORS

P. Hosseinzadeh Talaee

ABSTRACT

Streamflow forecasting has always been a challenging task for water resources engineers and managers. This study applies Multilayer Perceptron (MLP) networks optimized with three training algorithms, including resilient back-propagation (MLP_RP), variable learning rate (MLP_GDX), and Levenberg–Marquardt (MLP_LM), to forecast streamflow in Aspas Watershed, located in Fars province in southwestern Iran. The algorithms were trained and tested using 3 years of data. Antecedent streamflow with 1 day time lag constituted the first input vector, and MLP with this vector, labeled as MLP1 was the first model. Inclusion of streamflow with two, three, and four time lags led to input vectors 2, 3, and 4 which when combined with MLP resulted in MLP2, MLP3, and MLP4, respectively. It was found that the Levenberg–Marquardt algorithm performed best among three types of training algorithms employed for training the MLP models. Generally, the MLP4_LM model yields the best result with a determination coefficient and a root mean square error of 0.93 and 2.6 (m3/s). More... »

PAGES

695-703

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-012-1287-5

DOI

http://dx.doi.org/10.1007/s00521-012-1287-5

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50 schema:description Streamflow forecasting has always been a challenging task for water resources engineers and managers. This study applies Multilayer Perceptron (MLP) networks optimized with three training algorithms, including resilient back-propagation (MLP_RP), variable learning rate (MLP_GDX), and Levenberg–Marquardt (MLP_LM), to forecast streamflow in Aspas Watershed, located in Fars province in southwestern Iran. The algorithms were trained and tested using 3 years of data. Antecedent streamflow with 1 day time lag constituted the first input vector, and MLP with this vector, labeled as MLP1 was the first model. Inclusion of streamflow with two, three, and four time lags led to input vectors 2, 3, and 4 which when combined with MLP resulted in MLP2, MLP3, and MLP4, respectively. It was found that the Levenberg–Marquardt algorithm performed best among three types of training algorithms employed for training the MLP models. Generally, the MLP4_LM model yields the best result with a determination coefficient and a root mean square error of 0.93 and 2.6 (m3/s).
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