In brief
Indonesia’s National Research and Innovation Agency (BRIN) is creating models to forecast forest and land fires three to six months ahead, as directed by President Prabowo Subianto. The initiative will harness satellite imagery and partner with universities to refine fire‑spread predictions and improve preventive actions. BRIN will also continue daily hotspot monitoring and share data with the National Disaster Mitigation Agency and Forestry Ministry.
Indonesia’s National Research and Innovation Agency (BRIN) announced that it is developing advanced models to forecast forest and land fires up to six months in advance, a decision prompted by a directive from President Prabowo Subianto.
During a brief meeting held at the presidential palace in Jakarta on Tuesday, BRIN chief Arif Satria said the agency is already working on the models and is now validating their accuracy for 3‑month and 6‑month lead times.
The aim of the research is to strengthen medium‑ and long‑term fire management by providing authorities with early warnings that can trigger preventive measures before fires ignite.
In addition to predictive modeling, BRIN is studying the dynamics of how fires spread in areas affected by forest and land fires, a study that will help officials anticipate fire patterns and allocate resources strategically.
ARIF SARITA, a representative of BRIN, highlighted that the research is expected to enable authorities to anticipate fire spread and give government agencies more time to prepare.
Part of the initiative includes collaboration with universities, which will focus on fire‑forecasting research and investigation of fire‑spread patterns, according to ARIF SARITA.
BRIN has already been using satellite imagery for hotspot detection, monitoring fire occurrences each day and sharing the data with relevant agencies such as the National Disaster Mitigation Agency (BNPB) and the Forestry Ministry.
"We use the satellite imagery available at BRIN for various purposes, including detecting fire hotspots," ARIF SARITA explained.
Forecasting Process and Data
The models BRIN is developing will integrate satellite data on tree cover, temperature, rainfall, and topography to simulate fire risk over the next several months.
By ingesting these data streams in real time, the models can identify conditions that may lead to rapid fire development, such as dry vegetation combined with high temperatures.
Validation of the 3‑month and 6‑month predictions will involve comparing model outputs with observed fire incidents and satellite detections of active fires.
Once validated, these models are expected to be used by fire‑management teams to trigger evacuation plans, allocate firefighting resources, and issue warnings to communities near high‑risk areas.
The partnership with universities will facilitate research into fire‑spreading algorithms, potentially refining the models’ ability to predict the direction and speed of fire fronts.
This collaborative approach enables the exchange of academic expertise on atmospheric conditions and forest ecology with BRIN’s access to satellite platforms and computational infrastructure.
Implications for Fire Prevention and Response
Predicting fire risk months in advance represents a shift from reactive firefighting to proactive prevention, a necessary shift given Indonesia’s frequent forest and land fires.
Early warnings could help authorities cut down the time required to mobilize air and ground units, potentially shortening the duration of fires.
By providing a longer lead time, the models may also support upstream activities such as controlled burns, vegetation thinning, and public education campaigns, all of which reduce the likelihood of large fires.
Further, the more accurate the predictions, the better the allocation of limited resources, ensuring that emergency responders focus on the highest‑risk zones.
BRIN’s commitment to sharing daily hotspot data with the National Disaster Mitigation Agency and Forestry Ministry aligns the predictive models with operational realities on the ground.
Adopting satellite‑based monitoring allows for consistent, high‑resolution observations even in remote or hard‑to‑reach areas that often suffer from delayed fire detection.
By combining machine‑learning algorithms and atmospheric science, BRIN’s initiative aims to create a continuous monitoring and warning system capable of scaling across Indonesia’s vast forested regions.
In the longer term, the research could inform policy measures, such as zoning restrictions or stricter fire‑control regulations, by highlighting areas that consistently exhibit high fire potential.
Arif Satria indicated that the agency will use the insights from the findings to advise national fire‑management strategies and policymaking.
Overall, the predictive modeling effort seeks to transform how Indonesia tackles forest and land fires, drawing on technology and scientific collaboration to mitigate environmental damage.
