Nonparametric predictive inference forest fire dashboard - Amirah Hazwani, Roslin; Noryanti, Muhammad (Elsevier, 2024)
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.