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Nonparametric predictive inference forest fire dashboard

Forest fires have significantly increased over the years, leading to serious and expensive damages. These damages can be minimised, through information sharing before the forest fire occurrence using a dashboard. The existing forest fire dashboards’ main function is for monitoring based on past hist...

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Main Authors: Amirah Hazwani, Roslin, Noryanti, Muhammad
Format: Article
Language:English
Published: Elsevier 2024
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42938/1/Nonparametric%20Predictive%20Inference%20Forest%20Fire%20Dashboard.pdf
http://umpir.ump.edu.my/id/eprint/42938/
https://doi.org/10.1016/j.procs.2024.10.250
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spelling my.ump.umpir.429382025-01-14T04:53:11Z http://umpir.ump.edu.my/id/eprint/42938/ Nonparametric predictive inference forest fire dashboard Amirah Hazwani, Roslin Noryanti, Muhammad Q Science (General) QA Mathematics Forest fires have significantly increased over the years, leading to serious and expensive damages. These damages can be minimised, through information sharing before the forest fire occurrence using a dashboard. The existing forest fire dashboards’ main function is for monitoring based on past historical data. It will be helpful for many communities to know about the forest fire possibility of happening early for proper damage control precautions. Although the current forest fire prediction models are highly accurate, they still struggle with some issues. Hence, the Nonparametric Predictive Inference Forest Fire (NPIFF) dashboard was introduced in this paper to offer a new perspective on forest fire prediction study. It also included a predictive function using the R programming language software as its main task. The NPIFF dashboard utilised Malaysia's and Indonesia's past wildfire locations datasets to generate an imprecise probability of the next forest fire event. The results of a novel method, nonparametric predictive inference (NPI) with copula were displayed in the NPIFF dashboard using the new R algorithm. Then, the NPIFF dashboard was published online via the shinyapps.io server after it was successfully developed and functional. The information from the NPIFF dashboard can be relied on since it provides certainty confidence and portrayed highly accurate results which are useful for several organisations for various reasons. NPIFF dashboard will consider several improvements in the future to offer more information for more efficient forest fire prediction. Elsevier 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42938/1/Nonparametric%20Predictive%20Inference%20Forest%20Fire%20Dashboard.pdf Amirah Hazwani, Roslin and Noryanti, Muhammad (2024) Nonparametric predictive inference forest fire dashboard. Procedia Computer Science, 245. pp. 255-262. ISSN 1877-0509. (Published) https://doi.org/10.1016/j.procs.2024.10.250 10.1016/j.procs.2024.10.250
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Amirah Hazwani, Roslin
Noryanti, Muhammad
Nonparametric predictive inference forest fire dashboard
description Forest fires have significantly increased over the years, leading to serious and expensive damages. These damages can be minimised, through information sharing before the forest fire occurrence using a dashboard. The existing forest fire dashboards’ main function is for monitoring based on past historical data. It will be helpful for many communities to know about the forest fire possibility of happening early for proper damage control precautions. Although the current forest fire prediction models are highly accurate, they still struggle with some issues. Hence, the Nonparametric Predictive Inference Forest Fire (NPIFF) dashboard was introduced in this paper to offer a new perspective on forest fire prediction study. It also included a predictive function using the R programming language software as its main task. The NPIFF dashboard utilised Malaysia's and Indonesia's past wildfire locations datasets to generate an imprecise probability of the next forest fire event. The results of a novel method, nonparametric predictive inference (NPI) with copula were displayed in the NPIFF dashboard using the new R algorithm. Then, the NPIFF dashboard was published online via the shinyapps.io server after it was successfully developed and functional. The information from the NPIFF dashboard can be relied on since it provides certainty confidence and portrayed highly accurate results which are useful for several organisations for various reasons. NPIFF dashboard will consider several improvements in the future to offer more information for more efficient forest fire prediction.
format Article
author Amirah Hazwani, Roslin
Noryanti, Muhammad
author_facet Amirah Hazwani, Roslin
Noryanti, Muhammad
author_sort Amirah Hazwani, Roslin
title Nonparametric predictive inference forest fire dashboard
title_short Nonparametric predictive inference forest fire dashboard
title_full Nonparametric predictive inference forest fire dashboard
title_fullStr Nonparametric predictive inference forest fire dashboard
title_full_unstemmed Nonparametric predictive inference forest fire dashboard
title_sort nonparametric predictive inference forest fire dashboard
publisher Elsevier
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42938/1/Nonparametric%20Predictive%20Inference%20Forest%20Fire%20Dashboard.pdf
http://umpir.ump.edu.my/id/eprint/42938/
https://doi.org/10.1016/j.procs.2024.10.250
_version_ 1822924943483994112
score 13.23648

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