MACHINE LEARNING APPLICATION IN RESPONSE TO DISASTER RISK REDUCTION OF FOREST AND PEATLAND FIRE

Impact-Based Learning of DRR for Forest, Land Fire and Peat Smouldering

Abstract

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that has not been decomposed. Once it burns, smoldering peat fires consume huge biomass. Peat smoldering fires are challenging to extinguish. These will continuously occur for weeks to months. Experts and practitioners of peat smoldering fires are the most recommended effort 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, and soon such methods will be replaced by the Machine Learning method as it is developing recently. What about the potential application of Machine Learning in the forest and land fires, particularly smoldering peat fires in Indonesia? This paper tries to answer this question. This paper recommends a conceptual design: impact-based Learning for Disaster Risk Reduction (DRR) of Forest, Land Fire, and Peat Smouldering.