Land Subsidence and Coastal Flood Impact Scenarios Based on Remote Sensing in Selangor, Malaysia
Bibliographic record
Abstract
This study uses remote sensing data to assess susceptibility to hazards, which are then validated to model impact scenarios for land subsidence and coastal flooding in the Integrated Coastal Zone Management (ICZM) of Selangor, Malaysia, to support decision-making in urban planning and land management. Land subsidence and coastal floods affect a major proportion of the population in the ICZM, with subsidence being significant contributing factors, but information on the extent of susceptible areas, monitoring, and wide-area coverage is limited. Land subsidence distribution is demarcated using Interferometric Synthetic Aperture Radar (InSAR) time-series data (2015–2022), and integrated with coastal flood susceptibility derived from Analytic Hierarchy Process (AHP)-based weights to model impacts on land cover. Results indicate maximum subsidence rates of 46 mm/year (descending orbit) and 61 mm/year (ascending orbit); reflecting a gradual increase in subsidence trends with an average rate of 13 mm/year. In the worst-case scenario, within the ICZM area of 2262 km2, nearly 12% of the total built-up land cover with the highest population density is exposed to land subsidence, while exposure to coastal floods is relatively larger, covering nearly 34% of the built-up area. Almost 27% of the built-up area is exposed to the combined effects of both land subsidence and coastal floods, under present sea level conditions, with increasing risks of coastal floods over 2040, 2050 and 2100, due to both combinations. This research prioritizes areas for further study and provides a scientific foundation for resilience strategies aimed at ensuring sustainable coastal development within the ICZM.
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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 | 0.000 | 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 | 0.000 |
| 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".