Spatial assessment of coastal erosion vulnerability: Natural and anthropogenic factors in Chattogram, Bangladesh
Bibliographic record
Abstract
Bangladesh's coast, one of the world's most vulnerable, faces recurring natural disasters that cause significant casualties and damage. With rising sea levels and coastal hazards, assessing erosion vulnerability is crucial for protecting people and property. This paper aims to develop a location-specific index for Chattogram by integrating socioeconomic and physical factors to measure erosion vulnerability and support sustainable coastal management. In this study, the Coastal Erosion Vulnerability Index (CEVI) was quantified for the Chattogram coast using geospatial technologies. The CEVI was estimated using fifteen coastal Erosion vulnerability assessment factors which include seven physical and eight socio-economic factors. The data sources include the Alaska Satellite Facility, Geological Survey of Bangladesh, Bangladesh Agriculture Research Council, Esri Global Land Cover Map, WorldPop: Open Spatial Demographic Data and Research, Bangladesh Bureau of Statistics, Bangladesh Tourism Board, and the Local Government Engineering Department, etc. The Principal Component Analysis (PCA) method was used for this study to calculate the weightage of each factor. This study determines the spatial variations of the coastal vulnerability of Bangladesh. Physical vulnerability, driven by proximity to the coastline and soil texture, plays a dominant role, contributing 78.23% to overall vulnerability. Socio-economic vulnerability, influenced by factors like road buffer and literacy rate, contributes 21.77%. The most vulnerable areas encompass Sitakunda, Banshkhali, and Mirsharai upazilas, which require urgent interventions, at the same time as regions like Khulshi and Bayejid Bostami show low vulnerability. The model used is highly accurate, with an AUC of 0.909, offering reliable predictions for disaster preparedness and mitigation efforts, while ROC Curve and Precision Recall Analysis method was used for accuracy assessment. The model's vulnerability scores were validated against 185 ground-truth points sourced from field surveys, publications, and data provided by the Bangladesh Agricultural Research Council. The key findings of this study are very important to understand the impact and role of coastal vulnerability analysis on integrated coastal zone management planning. This study also helps future researchers to analyze coastal vulnerability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".