Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River
में बचाया:
| में प्रकाशित: | Hydrology vol. 12, no. 2 (2025), p. 25 |
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| मुख्य लेखक: | |
| अन्य लेखक: | , , , , |
| प्रकाशित: |
MDPI AG
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3171008111 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5338 | ||
| 024 | 7 | |a 10.3390/hydrology12020025 |2 doi | |
| 035 | |a 3171008111 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Mohammad Uzair Anwar Qureshi |u Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada; <email>mqure098@uottawa.ca</email> | |
| 245 | 1 | |a Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate. | |
| 651 | 4 | |a Mississippi Valley | |
| 651 | 4 | |a Ottawa River | |
| 653 | |a 100 year floods | ||
| 653 | |a River basins | ||
| 653 | |a Polynomials | ||
| 653 | |a Rivers | ||
| 653 | |a Water depth | ||
| 653 | |a Floodplains | ||
| 653 | |a Computer applications | ||
| 653 | |a Flood forecasting | ||
| 653 | |a Machine learning | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a River flow | ||
| 653 | |a Climate change | ||
| 653 | |a Datasets | ||
| 653 | |a Flood plain management | ||
| 653 | |a Climatic conditions | ||
| 653 | |a Accuracy | ||
| 653 | |a Clustering | ||
| 653 | |a Group method of data handling | ||
| 653 | |a Algorithms | ||
| 653 | |a Flood predictions | ||
| 653 | |a Flood management | ||
| 653 | |a Floods | ||
| 653 | |a Adaptability | ||
| 653 | |a Predictions | ||
| 653 | |a Environmental impact | ||
| 653 | |a Complexity | ||
| 653 | |a Synthetic data | ||
| 653 | |a Hydrology | ||
| 700 | 1 | |a Amiri, Afshin |u Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada; <email>afshin.amiri.1@ulaval.ca</email> (A.A.); <email>silvio-jose.gumiere@fsaa.ulaval.ca</email> (S.J.G.) | |
| 700 | 1 | |a Isa Ebtehaj |u Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada; <email>afshin.amiri.1@ulaval.ca</email> (A.A.); <email>silvio-jose.gumiere@fsaa.ulaval.ca</email> (S.J.G.) | |
| 700 | 1 | |a Guimere, Silvio José |u Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada; <email>afshin.amiri.1@ulaval.ca</email> (A.A.); <email>silvio-jose.gumiere@fsaa.ulaval.ca</email> (S.J.G.) | |
| 700 | 1 | |a Cunderlik, Juraj |u Mississippi Valley Conservation Authority, 10970 Hwy 7, Carleton Place, ON K7C 3P9, Canada; <email>jcunderlik@mvc.on.ca</email> | |
| 700 | 1 | |a Bonakdari, Hossein |u Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada; <email>mqure098@uottawa.ca</email> | |
| 773 | 0 | |t Hydrology |g vol. 12, no. 2 (2025), p. 25 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171008111/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171008111/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171008111/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |