Monitoring and Prediction of the Urmia Lake Drying Trend Based on Time-Series Remotely Sensed Images and Artificial Neural Networks
Bibliographic record
Abstract
Urmia Lake, which is the second largest permanent hypersaline lake in the world, is shrinking in recent decades. Since accurate spatial information about the lake is essential to managing the current and emerging crises of the lake, this study is used Urmia Lake satellite images time-series to investigate drought trends by analyzing via Artificial Neural Networks (ANN). The proposed approach is comprising the following four steps. First, yearly time-series Landsat images (2000-2022) are corrected geometrically and radiometrically. Then, time-series images of 2000-2020 are classified into five land cover classes, including; deep water, shallow water, salt, soil, and vegetation. In the third step, ANN trained for 2000-2019 as input and tested for 2020 as an output. Finally, the trained ANN is proposed to predict the future land covers of the lake (for 2021 and 2022 years). In order to evaluate the proposed model, the predicted maps of 2021 and 2022 were compared with their corresponding ground truth maps and quantitative criteria were calculated. The overall accuracy of the prediction for 2021 and 2022 is equal to 92.75% and 90.62%, respectively, which indicates the high capability of the proposed method for modeling and predicting changes in Urmia Lake and its shores.
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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 | 0.000 | 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".