Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information
Bibliographic record
Abstract
River water is one of the most important natural resources for economic development and environmental sustainability. However, river water systems are vulnerable in some of the densely populated regions across the globe. Intense sand mining and waste disposal can lead to river changes, loose foundations, and biodiversity loss. This study aims to develop a web-based geographic information system (GIS) platform to monitor river channel changes and their impacts on river environments due to sand mining and waste burial. The system integrates open-source software, Windows Server 2012, a web server, and PostgreSQL with PostGIS plugins for efficient mapping and storage of geospatial data and volunteered reporting of location events. Interferometric methods, including SNAP2STAMPS Automated Algorithm, persistent scatterer interferometry (PSI), small baseline subset (SBAS), and Snap software, were used to analyze spatial changes of subsidence from Sentinel-1 satellite data from 2021 to 2023 in the Gadar River in Oshnavieh, Iran. The results showed that the maximum subsidence at the riverbank was −10.1 cm due to sand mining, and the maximum uplift was 8.2 cm due to waste landfilling. The average subsidence was reported to be −5.1 cm. The results emphasize spatial analysis, showcasing material mining’s impact on subsidence trends and underscoring the significance of public participation in monitoring river health. Three parameters—completeness, correctness, and quality—were used to validate the system. Validation results showed completeness, correctness, and quality rates of 94.15%, 92.48%, and 86.63%, respectively.
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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 | 0.002 |
| 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".