TY - GEN
T1 - Soft Computing Techniques for Prediction of Forest Fire Occurrence in Brunei Darussalam
AU - Zahari, Muhammad Iskandar Hanafi Bin Pengiran Haji
AU - Karri, Rama Rao
AU - Isa, Mohamed Hasnain
AU - Mahmoud Zahran, El Said Mamdouh
AU - Shiva Nagendra, S. M.
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/1/10
Y1 - 2023/1/10
N2 - Forest fires are destroying wildlife habitat and pollutes air with emissions dangerous to human health. Increased carbon dioxide in the atmosphere by the wildfire contributes to the greenhouse effects and climate change. The ashes remove a lot of nutrients and eroded soils, causes landslides and flooding. Brunei Darussalam rich in biodiversity and tropical forest resources is increasingly recording more forest fires every year. These fires destroy the precious forest resources of the country, degrade the environmental quality particularly deteriorate air quality and cause significant economic loss in terms of property, infrastructure and possess threat to human health as well as ecosystem. Therefore, the objective of the study is to analyze the forest fire contributors such as topographic, human factor, climate, ignition factor, and vegetation and use a soft computing technique (machine learning) to classify the possible of occurrence.
AB - Forest fires are destroying wildlife habitat and pollutes air with emissions dangerous to human health. Increased carbon dioxide in the atmosphere by the wildfire contributes to the greenhouse effects and climate change. The ashes remove a lot of nutrients and eroded soils, causes landslides and flooding. Brunei Darussalam rich in biodiversity and tropical forest resources is increasingly recording more forest fires every year. These fires destroy the precious forest resources of the country, degrade the environmental quality particularly deteriorate air quality and cause significant economic loss in terms of property, infrastructure and possess threat to human health as well as ecosystem. Therefore, the objective of the study is to analyze the forest fire contributors such as topographic, human factor, climate, ignition factor, and vegetation and use a soft computing technique (machine learning) to classify the possible of occurrence.
KW - artificial neural network
KW - contributing factors
KW - Forest fires
KW - soft computing techniques
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85146530501&partnerID=8YFLogxK
U2 - 10.1063/5.0110349
DO - 10.1063/5.0110349
M3 - Conference contribution
AN - SCOPUS:85146530501
T3 - AIP Conference Proceedings
BT - 8th Brunei International Conference on Engineering and Technology 2021
A2 - Ali, Mohammad Yeakub
A2 - Karri, Rama Rao
A2 - Shams, Shahriar
A2 - Rosli, Roslynna
A2 - Rahman, Ena Kartina Abdul
A2 - Singh, Ramesh
PB - American Institute of Physics Inc.
T2 - 8th Brunei International Conference on Engineering and Technology 2021, BICET 2021
Y2 - 8 November 2021 through 10 November 2021
ER -
