PREDICTION OF POTENTIAL SEASONAL LAND AND FOREST FIRES (YEAR 2011 â 2022)
Forest and land fires are frequent disasters in Indonesia, particularly during the dry season, causing severe environmental, health, and economic impacts. Meteorological factors such as temperature, humidity, rainfall, and wind significantly influence the potential for fire occurrence. Seasona...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87987 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Forest and land fires are frequent disasters in Indonesia, particularly during the
dry season, causing severe environmental, health, and economic impacts.
Meteorological factors such as temperature, humidity, rainfall, and wind
significantly influence the potential for fire occurrence. Seasonal-scale fire
prediction can enhance preparedness and support decision-making in disaster
mitigation. Therefore, this study aims to evaluate the use of the Seasonal Severity
Rating (SSR) in characterizing fire potential and to develop a probabilistic SSR
prediction method for Kalimantan Island.
This study utilizes ECMWF Reanalysis Version 5 (ERA5) as observational data and
the Climate Forecast System Version 2 (CFSv2) as predictive data. The applied
method includes the calculation of the Fire Weather Index (FWI), which is further
developed into the Daily Severity Rating (DSR), Monthly Severity Rating (MSR),
and Seasonal Severity Rating (SSR). Additionally, a Time Lagged Ensemble
approach is used to improve prediction accuracy. The model's performance is
evaluated by comparing SSR predictions with actual fire occurrences and assessing
prediction reliability using the Brier Score (BS).
The results indicate that SSR can be used as an indicator of seasonal-scale forest
fire potential. The determination of SSR thresholds provides an overview of fire
severity levels in Kalimantan, establishing risk categories that can be incorporated
into early warning systems. SSR predictions using the CFSv2 model show promising
potential in forecasting seasonal fire conditions, although further development is
needed to enhance accuracy. Therefore, this study contributes to forest fire
mitigation efforts by providing a predictive method that can serve as a foundation
for forest and land fire management planning in Indonesia. |
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