TY - JOUR
T1 - The application of Bayesian model averaging based on artificial intelligent models in estimating multiphase shock flood waves
AU - Vosoughi, Foad
AU - Nikoo, Mohammad Reza
AU - Rakhshandehroo, Gholamreza
AU - Alamdari, Nasrin
AU - Gandomi, Amir H.
AU - Al-Wardy, Malik
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/11
Y1 - 2022/11
N2 - The multiphase shock wave phenomenon is significantly affected by accumulated upstream sediment deposition and downstream hydraulic conditions. There is a lack of studies evaluating the efficacy of intelligent models in representing multiphase debris flooding over initially dry- or wet-bed tail-waters, or over downstream semi-circular obstacles. To address this, we propose a novel methodology based on Bayesian Model Averaging (BMA), which combines predictions of three individual intelligent models [i.e., “Multi-layer Perceptron” (MLP), “Generalized Regression Neural Network”, and “Support Vector Regression”]. The models were developed through experimental study whereupon high-quality sediment depths and water levels data (n = 9000) were collected from 18 shock wave scenarios with various initial conditions in channel up- and down-stream. Experimental data and related original videos are created accessible in an online repository may be used in other researches. Each model’s results were in close concord with the experimental data; RMRE and RMSE values were in the range of 1.54–5.99 mm and 1.21–40.49 mm, respectively (0.5–2% and 0.4–13.5%) with the MLP model marginally outperforming the other intelligent models. Based on statistical error indices, the BMA model had the best performance (up to 40% better) in estimating most data classes, and was more efficient than the best intelligent model signifying that the proposed methodology is explicit, straightforward, and promising for real-world applications.
AB - The multiphase shock wave phenomenon is significantly affected by accumulated upstream sediment deposition and downstream hydraulic conditions. There is a lack of studies evaluating the efficacy of intelligent models in representing multiphase debris flooding over initially dry- or wet-bed tail-waters, or over downstream semi-circular obstacles. To address this, we propose a novel methodology based on Bayesian Model Averaging (BMA), which combines predictions of three individual intelligent models [i.e., “Multi-layer Perceptron” (MLP), “Generalized Regression Neural Network”, and “Support Vector Regression”]. The models were developed through experimental study whereupon high-quality sediment depths and water levels data (n = 9000) were collected from 18 shock wave scenarios with various initial conditions in channel up- and down-stream. Experimental data and related original videos are created accessible in an online repository may be used in other researches. Each model’s results were in close concord with the experimental data; RMRE and RMSE values were in the range of 1.54–5.99 mm and 1.21–40.49 mm, respectively (0.5–2% and 0.4–13.5%) with the MLP model marginally outperforming the other intelligent models. Based on statistical error indices, the BMA model had the best performance (up to 40% better) in estimating most data classes, and was more efficient than the best intelligent model signifying that the proposed methodology is explicit, straightforward, and promising for real-world applications.
KW - Abruptly changing topography
KW - Bayesian model averaging
KW - Experimental study
KW - High-quality data
KW - Intelligent models
KW - Multiphase shock wave
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UR - https://www.mendeley.com/catalogue/b925d75e-df61-30e3-b3c5-51e6c7c6173b/
U2 - 10.1007/s00521-022-07528-3
DO - 10.1007/s00521-022-07528-3
M3 - Article
AN - SCOPUS:85133645201
SN - 0941-0643
VL - 34
SP - 20411
EP - 20429
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 22
M1 - 22
ER -