Assessing Earthquake-Induced Sediment Accumulation and Its Influence on Flooding in the Kota Belud Catchment of Malaysia Using a Combined D-InSAR and DEM-Based Analysis
Bibliographic record
Abstract
A combined Differential InSAR (D-InSAR) and Digital Elevation Model (DEM)-based analysis revealed that earthquake-triggered landslides significantly altered river morphology and intensified flooding in the Kota Belud catchment, Sabah, Malaysia. This 1386 km2 catchment, home to about 120,000 people, has experienced a marked rise in flood events following the 4 June 2015 and 8 March 2018 earthquakes. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data and a 30 m Shuttle Radar Topography Mission (SRTM) DEM, complemented by river network information from HydroBASINS, were integrated to map sediment redistribution and model flood extent. Upstream zones exhibited extensive coseismic landslides and pronounced geomorphic disruption. Interferometric analysis showed that coherence was well preserved over stable terrain but rapidly degraded in vegetated and steep areas. Sediment aggradation, interpreted qualitatively from patterns of coherence loss and increased backscatter intensity, highlights slope failure initiation zones and depositional build-up along channels. Conversely, downstream, similar sedimentary adjustments were detected immediately upstream of areas with repeated flood incidents. Between 2015 and 2018, flood occurrences increased over fivefold, and after 2018, they increased by more than thirteenfold relative to pre-2015 conditions. DEM-based inundation simulations demonstrated that channel shallowing substantially reduced conveyance capacity and expanded flood extent. Collectively, these results confirm that earthquake-induced landslides have contributed to reshaping the geomorphology and amplified flooding in the area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".