Citation Export
DC Field | Value | Language |
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dc.contributor.author | Yi, Sooyeon | - |
dc.contributor.author | Yi, Jaeeung | - |
dc.date.issued | 2024-11-01 | - |
dc.identifier.issn | 2190-5495 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/34521 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206499780&origin=inward | - |
dc.description.abstract | In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, while GRU performed 4.69% (in R2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas. | - |
dc.language.iso | eng | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.subject.mesh | Data-driven approach | - |
dc.subject.mesh | Deep learning | - |
dc.subject.mesh | Flood forecasting | - |
dc.subject.mesh | Flood warning | - |
dc.subject.mesh | Leadtime | - |
dc.subject.mesh | Machine-learning | - |
dc.subject.mesh | Random forests | - |
dc.subject.mesh | Support vector regressions | - |
dc.subject.mesh | Travel-time | - |
dc.subject.mesh | Urban areas | - |
dc.title | Reservoir-based flood forecasting and warning: deep learning versus machine learning | - |
dc.type | Article | - |
dc.citation.number | 11 | - |
dc.citation.title | Applied Water Science | - |
dc.citation.volume | 14 | - |
dc.identifier.bibliographicCitation | Applied Water Science, Vol.14 No.11 | - |
dc.identifier.doi | 10.1007/s13201-024-02298-w | - |
dc.identifier.scopusid | 2-s2.0-85206499780 | - |
dc.identifier.url | https://www.springer.com/journal/13201 | - |
dc.subject.keyword | Data-driven approach | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Flood forecasting | - |
dc.subject.keyword | Lead time | - |
dc.subject.keyword | Machine learning | - |
dc.subject.keyword | Travel time | - |
dc.type.other | Article | - |
dc.identifier.pissn | 2190-5487 | - |
dc.description.isoa | true | - |
dc.subject.subarea | Water Science and Technology | - |
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