Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/8904
Title: Data pre-post processing methods in AI-based modeling of seepage through earthen dams
Authors: Sharghi, Elnaz
Nourani, Vahid
Behfar, Nazanin
Tayfur, Gökmen
Keywords: Artificial intelligence
Seepage
Ensemble method
Jittering
Mutual information
Issue Date: 2019
Publisher: Elsevier Ltd.
Abstract: In this paper, seepage of Sattarkhan earthen dam in northwest Iran was simulated using various artificial intelligence (AI) models (e.g., Feed forward neural network, Adaptive neural fuzzy inference system and Support vector regression) and linear ARIMA model based on different input combinations. Both jittering pre-processing and ensembling post-processing methods were also used in order to enhance the performance of the used AI-based data driven methods. For this purpose, various jittered datasets were produced by imposing noises (at different levels) to the original time series to enlarge the training data sample space. Further, three techniques of simple linear, weighted linear and nonlinear neural averaging were considered for pre-post processing purpose. The obtained results indicated that using both jittering and ensembling (especially neural ensemble) enhanced the modeling performance by almost 30% in the testing phase. (C) 2019 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.measurement.2019.07.048
https://hdl.handle.net/11147/8904
ISSN: 0263-2241
1873-412X
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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