Develop Smoke Detection Model Using GEMS to Respond Climate Change
Bibliographic record
Abstract
Wildfires have been an important factor affecting the Earth's surface and atmosphere for more than 350 million years [1].Wildfires can affect atmospheric conditions on a variety of spatial-temporal scales through the release of gases, particles, water and heat.Forest fires release a large amount of air pollutants, which cause climate change [2][3].The occurrence and intensity of wildfires are increasing with climate change [4].While this vicious cycle is repeated, Specific climate changes caused by emissions from wildfire smoke include changes in the land-atmosphere system due to greenhouse gases and a catalytic role in the formation of cloud condensation nuclei [5].The use of satellite product and machine learning is essential for detection of forest fire smoke.Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries.The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning.In March 2022, the forest fire in Gangwon-do was selected as a case.And, we created two random forest model that smoke pixel classification model and regression model were performed by injecting GEMS Level 1 and Level 2 data.At this time, the input variables of the regression model were adjusted due to the problem of missing values in certain data.In the classification model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO2), 380 nm radiance, and 340 nm radiance were shown in that order.Also, the smoke classification accuracy for 2,704 pixels showed the level of Accuracy = 0.998 and mIoU = 0.995.The input variable importance of the regression model is appeared in the order of FCC Grayscale image, Ultra-Violet Aerosol Index, radiance difference between 380 nm and 340 nm, Visible Aerosol Index, and formaldehyde.In addition, smoke probability for 4,695 pixels (0≤p≤1) are Mean Bias Error (MBE) was -0.001, Mean Absolute Error (MAE) was 0.028, Root Mean Square Error (RMSE) was 0.113, and Correlation Coefficient (CC) was 0.974.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".