Soft computing techniques for prediction of forest fire occurrence in Brunei Darussalam

Muhammad Iskandar Hanafi Bin Pengiran Haji Zahari, Rama Rao Karri, Mohamed Hasnain Isa, Elsaid Mamdouh Mahmoud Zahran, S. M. Shiva Nagendra

Research output: Journal PublicationArticlepeer-review

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Forest fires are destroying wildlife habitats and polluting the air with emissions dangerous to human health. Increased carbon dioxide in the atmosphere by wildfire contributes to the greenhouse effects and climate change. The ashes remove a lot of nutrients and erode soils, causing landslides and flooding. Brunei Darussalam is rich in biodiversity, and tropical forest resources are increasingly recording more forest fires yearly. These fires destroy the precious forest resources of the country, degrade the environmental quality, particularly deteriorating air quality and cause significant economic loss in terms of property and infrastructure, and possess a threat to human health as well as the ecosystem. Therefore, the objective of the study is to analyze forest fire contributors such as topographic, human factors, climate, ignition factors, and vegetation using a soft computing technique (machine learning) to predict the occurrence of forest fires.
Original languageEnglish
Pages (from-to)030023
Number of pages8
JournalAIP Conference Proceedings
Issue number1
Publication statusPublished - 10 Jan 2023


  • Forest fires
  • contributing factors
  • soft computing techniques
  • artificial neural network
  • support vector machine


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