Title | ||
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The application of Bayesian model averaging based on artificial intelligent models in estimating multiphase shock flood waves |
Abstract | ||
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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. |
Year | DOI | Venue |
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2022 | 10.1007/s00521-022-07528-3 | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Multiphase shock wave, Bayesian model averaging, Intelligent models, Experimental study, High-quality data, Abruptly changing topography | Journal | 34 |
Issue | ISSN | Citations |
22 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
f vosoughi | 1 | 0 | 0.34 |
mr nikoo | 2 | 0 | 0.68 |
g rakhshandehroo | 3 | 0 | 0.34 |
n alamdari | 4 | 0 | 0.34 |
Amir Hossein Gandomi | 5 | 1836 | 110.25 |