Remote sensing-based shoreline change investigation in Klang Coast and Langkawi Island towards sustainable coastal management
Bibliographic record
Abstract
Malaysia’s coastline is incessantly exposed to coastal hazards, sea-level rise (SLR), and coastal erosion. A quantitative examination of shoreline migration patterns over various timeframes is necessary to comprehend land–sea interface behaviour and coastal ecology. Due to gradual changes in sea currents and coastline, Langkawi International Airport was constructed in December 1993 and extended near the coast in 2006. From 1994 to 2024, this study aims to manage changes in shorelines of Klang, Malaysia, and Langkawi Island, Malaysia, using Landsat 4-5 Thematic Mapping (TM) and 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS), Digital Shoreline Analysis System (DSAS), and ArcGIS software. Langkawi Island’s endpoint rates (EPR) varied between −1.34 and 1.02 m/year, whereas linear regression rate (LRR) was found to be 1.04 m/year for accretion and −0.84 m/year for erosion. Similarly, the Klang coast recorded −1.46 to 1.76 m/year LRR and −1.19 to 3.44 m/year EPR. While Northport (Malaysia) Bhd. seeing accretion due to building, Kapar Energy Ventures mostly witnessed erosion. The results highlight critical need for specialized coastal management techniques, like building seawalls, regenerating mangroves, and managing sediments, to reduce erosion and improve shoreline stability. To make well-informed decisions on sustainable coastal development, this study offers fresh perspectives on spatiotemporal Malaysian coastline dynamics.
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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 | 0.000 |
| 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 | 0.000 | 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 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".