Forest fire hazard rating assessment mapping in Sabah, Malaysia
Bibliographic record
Abstract
Forest fires can dramatically affect the ecosystem and has a great impact on the human life as well. In Malaysia, especially Sabah, forest fires has become a serious phenomenon recorded since early 1998. In order to reduce the threat of forest fires incidence and avoid any potential damage, it is very crucial to carry out an assessment of the forest fires hazard rating. This study is based on three objectives; to identify the hotspot patterns, to analyze the Fire Weather Index (FWI) trend for five years period (2006 - 2010) for Sabah and to generate the maps of forest fire hazard zone for the state of Sabah. The hotspot data were obtained from the Agency Remote Sensing Malaysia (ARSM) and Moderate Resolution Imaging Spectroradiometer (MODIS). Weather data was obtained from Malaysian Meteorological Department and Sabah Forestry Department for analysis and deriving of the Canadian Forest Weather Index (CFWI). Hotspot density analysis was carried out for five years period to observe for the annually and monthly hotspot pattern and also by division in the state. In forest fires hazard mapping, data of forest type, road, town, river and hotspot point were developed as layers using Geographical Information System (GIS) software. The Weighted Overlay Analysis were used to composite and generate five categories ranging from the very high fire hazard to the very low fire hazard. Results showed that, during the study, the highest hotspots were obtained in March 2010 with 445 hotspots, and the lowest was in January 2009 where no hotspot was detected. Then, the interior registered the highest number of hotspots with 1159 hotspots followed by 697 in Sandakan, 475 in the West Coast South, 327 in the West Coast North and 226 in Tawau. In the FWI analysis, Kudat station had the highest Extreme, High and Medium fire danger indices with 22 days, 140 days and 440 days respectively during the study period. In forest fire hazard map, about 53 % of the study areas have been classified as low risk, 35 % medium and only 1 % classified as very high risk to forest fire incident. Lastly, forest fire hazard map was validated with past fire incidences data that was collected from Forestry Department of Sabah website. The results of the study showed that out of 15 fire incidences in 2011 and 2012, 7 incidences had occurred in very high and high risk areas. As a result, the fire hazard map can be used to improve the forest fire management more effectively and systematically.
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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 | 0.000 |
| 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 | 0.001 | 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".