Integration of SETS (Social–Ecological–Technological Systems) Framework and Flood Resilience Cycle for Smart Flood Risk Management
Bibliographic record
Abstract
The concept of “water smart city” is increasingly being recognized as a new approach to managing urban environments (including urban floods), especially in the context of developing countries, such as Indonesia. While Indonesia’s national capital relocation plan is expected to attract significant human migration to two nearby cities, Samarinda City and the port city of Balikpapan, these cities have continuously faced with severe risk of flooding. Therefore, this research proposes a flood management approach by reviewing the local city government’s flood risk management strategies and the smart city plan to enhance flood resilience. The integration of the SETS (Social–Ecological–Technological systems) framework and the Flood Resilience Cycle is undertaken to determine the state of flood management, which is followed by a review of smart city plans and programs in two selected cities (Samarinda and Balikpapan). The research mainly identifies how it can be implemented in the two selected cities based on SETS–FRC distribution. In accordance with the SETS–FRC (Flood Resilience Cycle) framework, it is revealed that both these cities have a higher emphasis on the flood prevention phase, as compared to other resilience phases. Based on the overall results, this study emphasizes the implementation of a water smart city concept for effective and smart flood risk management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".