Affiliations 

  • 1 Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia. nurulnnadiah94@gmail.com
  • 2 Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
  • 3 Information System Department, Universitas Islam Indragiri, Tembilahan, 29212, Indonesia
  • 4 Informatics Engineering Department, Universitas Islam Riau, Pekanbaru, 28284, Indonesia
  • 5 Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia
  • 6 Centre of Excellence in Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Kuantan, Pahang Darul Makmur, Malaysia
  • 7 School of Computing, Telkom University, Bandung, 40257, Indonesia
PMID: 39037622 DOI: 10.1007/s11356-024-34409-0

Abstract

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC's 0.0914 seconds and QMC's 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.