ArticleScientific reports2023
Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping.Scientific reports · 2026Article
- Annotated data for semantic role labeling of crisis events in Indonesian Tweets.Data in brief · 2025Article
- Projecting the effect of climate change on multiple Geomorphological hazard using machine learning data driven approaches.Scientific reports · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous natural disasters that threaten people's lives and property occur in Indonesia. Climate change-induced temperature increases are expected to affect the frequency of natural hazards in the future and pose more risks. This study examines the consequences of droughts and forest fires on the Indonesian island of Kalimantan. We first create maps showing the eleven contributing factors that have the greatest impact on forest fires and droughts related to the climate, topography, anthropogenic, and vegetation. Next, we used RF to create single and multi-risk maps for forest fires and droughts in Kalimantan Island. Finally, using the Coupled Model Intercomparison Project (CMIP6) integrated evaluation model, a future climate scenario was applied to predict multiple risk maps for RCP-SSP2-4.5 and RCP-SSP5-8.5 in 2040-2059 and 2080-2099. The probability of a 22.6% drought and a 21.7% forest fire were anticipated to have an influence on the study's findings, and 2.6% of the sites looked at were predicted to be affected by both hazards. Both RCP-SSP2-4.5 and RCP-SSP5-8.5 have an increase in these hazards projected for them. Researchers and stakeholders may use these findings to assess risks under various mitigation strategies and estimate the spatial behavior of such forest fire and drought occurrences.
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Registered trials
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