Forest Fire Incident Forecasting System In Permanent Reserved Forest In Peninsular Malaysia Using Big Data Analytics

Abstract

Purpose – This paper summarized the research project on the implementation of big data analytics to forecast the forest fire incident in Peninsular Malaysia. Design/methodology/approach – The research project has developed a Forest Fire Incident Forecasting System in Permanent Reserves Forest (PRF) to forecast forest fire incidence in Peninsular Malaysia. This project was conducted for the Forestry Department Peninsular Malaysia (JPSM) in collaboration with the Ministry of Natural Resources, Environment and Climate Change (NRECC) using Big Data Analytics (BDA). Findings – The results from the system have been summarized into four conclusions. Firstly, the forecast of areas with fire potential can be identified as early as 7 days; secondly, the location of relevant agencies to deal with forest fires close to the site of the fire incident can be identified. Third, the water source close to the fire scene can be located; and finally, the estimated cost of the extinguishing operation can be determined in advance. Practical Implications - To overcome these obstacles and accomplish wise forest management, modern science, and technology must be improved. This research project has successfully implemented BDA via the Forest Fire Incident Forecasting System in PRF. It has improved the performance of the JPSM for its forest management system. Originality – The project is originally conducted with collaborations between the JPSM and NRECC in the Malaysian federal government and has been successfully implemented by the forestry department.