Resilient Stormwater Management of a Coastal Catchment
Bibliographic record
Abstract
Designing stormwater drainage systems considering deep uncertainty is a task that has no correct solution, rather, it can only be addressed by managing the system in a smart, robust way. Over recent decades, robust decision-making has been promoted as a solution to planning systems that are vulnerable to deep uncertainty. In this paper, we adopt a robust decision-making methodology to propose a stormwater drainage system in a coastal catchment in an arid region which is vulnerable to sea-level rise and increased precipitation intensity. We used bias-corrected precipitation and sea-level rise projections from a regional climate model, in addition to analyzing observed data. The decision-making methodology adopted is the Dynamic Adaptive Policy Pathways (DAPP). It involves building a decision tree with probable actions to consider when the stormwater system is expected to fail. The success of DAPP relies on continuous and extensive monitoring of the system and all components/factors that form risk or add to the vulnerability of the system, in addition to extensive simulations of the pre-identified actions that enable quick implementation of the solutions preceding failure of the system. This gives an early warning and aids the proactive execution of actions, hence making the system resilient to deep uncertainty. The DAPP for the study site is presented, and the advantages of relying on robust decision-making for arid regions are discussed.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".