Assessing the Risk of Inundation in Thu Duc City using an Integrated 1D-2D Hydrodynamic Model with a Combination of Boundary Conditions Defined by Probability Analyses
Bibliographic record
Abstract
Urban flooding has emerged as a significant issue in rapidly expanding cities like Thu Duc City, Vietnam, exacerbated by the dual pressures of urbanization and climate change. This study employs an integrated 1D-2D hydrodynamic model to assess flood risks and hazards, incorporating probabilistic analyses of rainfall and tidal levels. Simulations across varying recurrence intervals produce detailed flood hazard maps that identify vulnerable areas and quantify flood depths. Key findings indicate that central areas, including Thu Duc market and Tam Binh Ward, are highly prone to flooding, with depths exceeding 1.5 meters in severe scenarios. The flood hazard maps reveal consistent flooding patterns, with both flood depths and affected areas increasing over time. Economic assessments estimate average annual flood losses at 21,622 billion Vietnamese dong (VND), underscoring the substantial economic consequences of flooding in the region. This study uniquely integrates multi-factor boundary conditions, combining rainfall and tidal influences for a more comprehensive risk assessment than traditional single-factor approaches. The findings highlight the urgency of enhancing drainage infrastructure and implementing targeted flood management strategies, such as retention basins and improved urban planning. These results offer critical insights for urban planners and policymakers aiming to mitigate flood risks and enhance resilience in rapidly urbanizing areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".