Using Artificial Intelligence Technique in Estimating Fire Hotspots of Forest Fires. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Agustiyara, ; Purnomo, Eko Priyo; Ramdani, Rijal
• Is Part Of: IOP conference series. Volume 717:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Forest Fires -- AI -- Estimation -- Hotspot; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/717/1/012019
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: This paper aims to assess the fire detection systems in estimating hotspots in forest fires, in other words, a way of considering the possible scale of fires. Since it needs to have precise and fast mechanisms to make the right decision in case of a forest fire. In this paper, the hotspot resulted from potential forest fires was estimated using the Artificial Intelligence (AI) technique, which contained certain parameters, such as time, when the fire broke out, and unit area of the existing environment. Fire estimation can be built as a large-scale framework that gathers hotspot data from multiple regions. The current estimation systems, such as sipongi.menlhk.go.id and geospasial.bnpb.go.id as forest fire databases, are used to identify forest fire possibility and risk at any given time. The data was from the SiPongi and BNPB in Indonesia and contained forest fire hotspot records from 2010 and 2020. The output from the estimation methods applied in this paper predicted the scale of the hotspots i.e., large, medium, or small fire. Furthermore, the Geographical Information System (GIS) based model was used to calculate the forest fire hotspot, landscape, and topographic data in the selected provinces. In this case, AI is used to classify the regions at risk of forest fires and estimate the burned area for recent forest fires. The results of these estimates are presented and compared to similar studies in the literature.