Assessing the Tidal Influences on the Coastal Erosion-Accretion Processes in the Indian Sundarban Delta using Hydrodynamic Models and Geoinformatics
Bibliographic record
Abstract
Abstract The Sundarbans is currently a subject of serious concern due to the massive coastline erosion that is responsible for reshaping the geomorphological content of the islands. The coastal islands of the Sundarbans have become more susceptible to sea-level rise, cyclonic storms, and coastal flooding, which further accelerate the process of coastal erosion. Identification of erosion-prone regions is highly essential for integrated coastal zone management. Till date, no scientific study has been done to relate the coastal erosion accretion process with tidal velocity and current direction in the Indian Sundarbans. In this perspective, the present study identifies the erosion-accretion zones in the Indian Sundarbans using Landsat imageries of 2000 and 2020 using geoinformatics. It investigates the impact of tidal velocity and direction on the erosion accretion processes. MIKE 21 two-dimensional hydrodynamic model was simulated to predict tidal velocities and directions. The study finds that the sea-facing islands of the Indian Sundarbans experience the maximum amount of erosion. A significant shrinking of the land area takes place in the Bulchery, Bhangaduiani, and Dalhousie islands. The rates of erosion of these islands are 485.99 m/year, 487 m/year, and 480.65 m/year, respectively. Places with high velocity are found to be erosion-prone, while the rate of erosion significantly varied with the flow directions. Two islands, Bulchery and Dalhousie, on the south-eastern margin of the delta, suffered maximum erosion over the past decades due to the impact of high-velocity currents from the southeast and southwest, respectively. Alternatively, minor accretions were observed along with the sheltered island precincts where velocities were comparatively low. Such studies on the current drive erosion–accretion processes along the delta margin during sea-level rise appear to be of utmost importance for sustainable coastal protection and management.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".