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Uncertainty quantification of granular computing‑neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams
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Ghiasi, Behzad, Noori, Roohollah, Sheikhian, Hossein, Zeynolabedin, Amin, Sun, Yuanbin, Jun, Changhyun, Hamouda, Mohamed, Bateni, Sayed M. and Abolfathi, Soroush (2022) Uncertainty quantification of granular computing‑neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams. Scientific Reports, 12 . 4610. doi:10.1038/s41598-022-08417-4 ISSN 2045-2322.
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WRAP-Uncertainty-quantification-granular-neural-network-model-prediction-pollutant-streams-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2624Kb) | Preview |
Official URL: https://doi.org/10.1038/s41598-022-08417-4
Abstract
Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems.
| Item Type: | Journal Article | ||||||
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| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TD Environmental technology. Sanitary engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Rivers, Water -- Pollution, Water -- Pollution -- Computer programs, Water -- Pollution -- Measurement, Water quality , Water quality -- Measurement -- Computer simulation | ||||||
| Journal or Publication Title: | Scientific Reports | ||||||
| Publisher: | Nature Publishing Group | ||||||
| ISSN: | 2045-2322 | ||||||
| Official Date: | 17 March 2022 | ||||||
| Dates: |
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| Volume: | 12 | ||||||
| Article Number: | 4610 | ||||||
| DOI: | 10.1038/s41598-022-08417-4 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 18 March 2022 | ||||||
| Date of first compliant Open Access: | 21 March 2022 |
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