Earthfill dam seepage analysis using ensemble artificial intelligence based modeling

• Published in: Journal of hydroinformatics Vol. 20; no. 5; pp. 1071 - 1084
• Main Authors: Sharghi, Elnaz, Nourani, Vahid, Behfar, Nazanin
• Format: Journal Article
• Language: English
• 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
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2018.151

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.