Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment
The paper applies soft computing techniques to contaminant transport modeling in river systems and focuses on the Monocacy River. The research employed various techniques, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), and...
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Shahid Chamran University of Ahvaz
2024-07-01
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| Series: | Journal of Hydraulic Structures |
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| Online Access: | https://jhs.scu.ac.ir/article_19417_72369b71e12902bbbe83a2e919dff284.pdf |
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| author | Jafar Chabokpour |
| author_facet | Jafar Chabokpour |
| author_sort | Jafar Chabokpour |
| collection | DOAJ |
| description | The paper applies soft computing techniques to contaminant transport modeling in river systems and focuses on the Monocacy River. The research employed various techniques, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), and Genetic Algorithms (GA), to predict pollutant concentrations and estimate transport parameters. The ANN, particularly the Long Short-Term Memory architecture, had more superior performance: the lowest RMSE of 0.37, and the highest R-squared was 0.958. The RMSE obtained by the ANFIS model was 0.40, with an R-squared value of 0.945. It provided a balance with accuracy and interpretability. SVR performance with RBF kernel was robust; it has attained an RMSE of 0.42 and R-squared of 0.940, along with very fast training times. The flow velocities and the longitudinal dispersion coefficients at different reaches were estimated to be in the range of 0.30 to 0.42 m/s for average flow velocity and 0.18 to 0.31 m²/s for the longitudinal dispersion coefficient. In addition, the potentially affected fraction of species due to peak concentrations was used to reflect the assessment of ecological impact, which had values ranging from 0.07 to 0.35. For the time-varying estimation, there is supposed to be a variation in the dispersion coefficient and the decay rate over 48 hours, from 0.75 to 0.89 m²/s and from 0.10 to 0.13 day⁻¹, respectively. The research demonstrates the potential of soft computing approaches for modeling complex pollutant dynamics and further provides valuable insights into river management and environmental protection strategies. |
| format | Article |
| id | doaj-art-0ab85a02092a411489eda8d4be8fe99a |
| institution | OA Journals |
| issn | 2345-413X 2345-4156 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Shahid Chamran University of Ahvaz |
| record_format | Article |
| series | Journal of Hydraulic Structures |
| spelling | doaj-art-0ab85a02092a411489eda8d4be8fe99a2025-08-20T02:23:44ZengShahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X2345-41562024-07-01104829610.22055/jhs.2024.47440.131219417Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact AssessmentJafar Chabokpour0Civil engineering department, University of Maragheh, Maragheh, Iran.The paper applies soft computing techniques to contaminant transport modeling in river systems and focuses on the Monocacy River. The research employed various techniques, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), and Genetic Algorithms (GA), to predict pollutant concentrations and estimate transport parameters. The ANN, particularly the Long Short-Term Memory architecture, had more superior performance: the lowest RMSE of 0.37, and the highest R-squared was 0.958. The RMSE obtained by the ANFIS model was 0.40, with an R-squared value of 0.945. It provided a balance with accuracy and interpretability. SVR performance with RBF kernel was robust; it has attained an RMSE of 0.42 and R-squared of 0.940, along with very fast training times. The flow velocities and the longitudinal dispersion coefficients at different reaches were estimated to be in the range of 0.30 to 0.42 m/s for average flow velocity and 0.18 to 0.31 m²/s for the longitudinal dispersion coefficient. In addition, the potentially affected fraction of species due to peak concentrations was used to reflect the assessment of ecological impact, which had values ranging from 0.07 to 0.35. For the time-varying estimation, there is supposed to be a variation in the dispersion coefficient and the decay rate over 48 hours, from 0.75 to 0.89 m²/s and from 0.10 to 0.13 day⁻¹, respectively. The research demonstrates the potential of soft computing approaches for modeling complex pollutant dynamics and further provides valuable insights into river management and environmental protection strategies.https://jhs.scu.ac.ir/article_19417_72369b71e12902bbbe83a2e919dff284.pdfcontaminant transport modelingsoft computing methodsartificial neural networksriver system dynamics |
| spellingShingle | Jafar Chabokpour Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment Journal of Hydraulic Structures contaminant transport modeling soft computing methods artificial neural networks river system dynamics |
| title | Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment |
| title_full | Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment |
| title_fullStr | Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment |
| title_full_unstemmed | Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment |
| title_short | Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment |
| title_sort | advanced soft computing ensemble for modeling contaminant transport in river systems a comparative analysis and ecological impact assessment |
| topic | contaminant transport modeling soft computing methods artificial neural networks river system dynamics |
| url | https://jhs.scu.ac.ir/article_19417_72369b71e12902bbbe83a2e919dff284.pdf |
| work_keys_str_mv | AT jafarchabokpour advancedsoftcomputingensembleformodelingcontaminanttransportinriversystemsacomparativeanalysisandecologicalimpactassessment |