Earthfill dam seepage analysis using ensemble artificial intelligence based modeling

In this paper, an ensemble artificial intelligence (AI) based model is proposed for seepage modeling. For this purpose, firstly several AI models (i.e. Feed Forward Neural Network, Support Vector Regression and Adaptive Neural Fuzzy Inference System) were employed to model seepage through the Sattar...

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Bibliographic Details
Published inJournal of hydroinformatics Vol. 20; no. 5; pp. 1071 - 1084
Main Authors Sharghi, Elnaz, Nourani, Vahid, Behfar, Nazanin
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.09.2018
Subjects
Adaptive systems
Artificial intelligence
Dams
Damsites
Earth dams
Finite element analysis
Fuzzy logic
Fuzzy systems
Hydraulics
Methods
Modelling
Neural networks
Performance prediction
Post-production processing
Regression analysis
Runoff
Seepage
Support vector machines
Time series
Poland
New York
Iran
United States > US
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Summary:In this paper, an ensemble artificial intelligence (AI) based model is proposed for seepage modeling. For this purpose, firstly several AI models (i.e. Feed Forward Neural Network, Support Vector Regression and Adaptive Neural Fuzzy Inference System) were employed to model seepage through the Sattarkhan earthfill dam located in northwest Iran. Three different scenarios were considered where each scenario employs a specific input combination suitable for different real world conditions. Afterwards, an ensemble method as a post-processing approach was used to improve predicting performance of the water head through the dam and the results of the models were compared and evaluated. For this purpose, three methods of model ensemble (simple linear averaging, weighted linear averaging and non-linear neural ensemble) were employed and compared. The obtained results indicated that the model ensemble could lead to a promising improvement in seepage modeling. The results indicated that the ensembling method could increase the performance of AI modeling by up to 20% in the verification step.
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ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2018.151