Contribution of Deforestation to Severe Flooding in Southeast Parts of the Caspian Sea: A Case Study with NDVI Analysis
Bibliographic record
Abstract
Iran has been faced with increase in flooding cases during the past 60 years. The human activities have been considered as a devastating factor in the environmental change causing the occurrence of severe flooding cases during past decades. On August 11, 2001, a relatively severe rainfall in the south east of Caspian Sea led to the occurrence of a severe deadly flooding in Golestan province and some parts in northern Khorasan province have been unprecedented in Iran over the past century. The destructive extent of flooding in the urban and rural areas reached about 5,000 km 2 . Here, the synoptic surface and upper levels of the weather charts have been analyzed along with the monitoring of half hourly METEOSAT7 images to show the convective clouds development over the area of the study. The total precipitation in this area during the flooding period was reported between 2.5 and 153 mm with the maximum estimation over the center of the storm around less than 250[Formula: see text]mm. Using satellite imagery in 1979 and 2000, vegetation changes and environmental changes have been investigated and shown extensive decline in vegetation. The image processing and Normalized Difference Vegetation Index (NDVI) calculation of the color composite 433 of LANDSAT5 and the color composite 211 of TERRA (MODIS sensor) images between 1998 and 2001 have been revealed significant deforestation around 248,131,534.3[Formula: see text][Formula: see text] over the study, particularly over the rivers’ neighborhood. Also, by assumption of the same precipitation for 1998 and 2001, the discharge rate in flood case of 2001 has been intensified 1.3 times (at 13 percent) larger than that of 1998. This shows the direct impact of the deforestation and land use changes over the study area during 1998–2001.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".