Machine Learning Application in Response to Disaster Risk Reduction of Forest and Peatland Fire

Abstract

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that have not been decomposed. In the dry season, this accumulated biomass called peat, which was originally submerged in water, is exposed to the surface and prone to fire. Once it burns, smouldering peat fires consume biomass 15 times larger than open flame. 
 Peat smouldering fires are very difficult to extinguish. These will continuously occur for weeks to months. The most recommended effort by experts and practitioners of peat smouldering fires is to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology'. Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses (1) the accuracy is very low; (2) having predictive biases in areas with different ecosystem characteristics; (3) it is often difficult to implement because it requires a large and complex number of 'expert rules'; and (4) involving many variables, complex, and heterogeneous data formats.
 Slowly but surely this method has been replaced by the Machine Learning (ML) method as it is developing in Europe, China, India, Japan, North America and Australia. What about the potential application of ML in the forest and land fires, especially smouldering peat fires in Indonesia? This paper tries to answer this question through the following points:
 
 State of the art of machine learning in science and management of forest and land fire
 Outlook on technology of Disaster Risk Reduction (DRR) in BPPT in the field of forest and land fire and the opportunities of ML implementation 
 Impact based forecasting and machine learning for DRR of forest and land fire
 
 This paper recommends a conceptual design: Impact-Based Learning for DRR of Forest, Land Fire and Peat Smouldering

Social Capital of Banjarese for Peatland Fire Mitigation: Combining of Local Wisdom and Environment

“…Peatlands are natural ecosystems that contain stored biomass from biomass deposits originating from past tropical swamp vegetation that has not been decomposed. Burning peatlands consumes a huge amount of biomass and causes fires that are difficult to extinguish [1]. Peatland fires have an impact on the environment and people's livelihoods [2].…”