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利用随机森林算法对印度尼西亚加里曼丹岛在气候危机下的多灾种暴露情况进行绘图。

Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia.

作者信息

Heo Sujung, Park Sangjin, Lee Dong Kun

机构信息

Interdisciplinary Program and Life Science, Seoul National University, Seoul, Korea.

Korea Institute of Public Administration, Seoul, Korea.

出版信息

Sci Rep. 2023 Aug 18;13(1):13472. doi: 10.1038/s41598-023-40106-8.

DOI:10.1038/s41598-023-40106-8
PMID:37596300
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10439166/
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.

摘要

印度尼西亚发生了许多威胁人们生命和财产的自然灾害。预计气候变化导致的气温上升将影响未来自然灾害的发生频率,并带来更多风险。本研究考察了印度尼西亚加里曼丹岛干旱和森林火灾的后果。我们首先绘制地图,展示对与气候、地形、人为因素和植被相关的森林火灾和干旱影响最大的11个促成因素。接下来,我们使用随机森林(RF)为加里曼丹岛的森林火灾和干旱创建单一风险和多风险地图。最后,使用耦合模式比较计划(CMIP6)综合评估模型,应用未来气候情景预测2040 - 2059年和2080 - 2099年RCP - SSP2 - 4.5和RCP - SSP5 - 8.5的多重风险地图。预计22.6%的干旱概率和21.7%的森林火灾概率会对研究结果产生影响,预计所考察地点的2.6%会受到这两种灾害的影响。预计RCP - SSP2 - 4.5和RCP - SSP5 - 8.5的这些灾害都会增加。研究人员和利益相关者可以利用这些结果评估各种缓解策略下的风险,并估计此类森林火灾和干旱事件的空间行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/c2aceaec0e34/41598_2023_40106_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/72d98ca918b5/41598_2023_40106_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/d01d556ce7d3/41598_2023_40106_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/0466a9e99cae/41598_2023_40106_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/c2aceaec0e34/41598_2023_40106_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/72d98ca918b5/41598_2023_40106_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/5dfaf0e6c5dc/41598_2023_40106_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/3104cfb55cce/41598_2023_40106_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/b5266fd57747/41598_2023_40106_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/d01d556ce7d3/41598_2023_40106_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/0466a9e99cae/41598_2023_40106_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65b8/10439166/c2aceaec0e34/41598_2023_40106_Fig7_HTML.jpg

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本文引用的文献

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2
Assessing costs of Indonesian fires and the benefits of restoring peatland.评估印度尼西亚火灾的成本和恢复泥炭地的效益。
Nat Commun. 2021 Dec 2;12(1):7044. doi: 10.1038/s41467-021-27353-x.
3
A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran.
加里曼丹和苏门答腊洪水与山体滑坡风险的多灾种评估:对印度尼西亚新首都努米亚的影响
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伊朗多灾害地图:洪水、沟蚀、森林火灾和地震。
Sci Rep. 2021 Jul 21;11(1):14889. doi: 10.1038/s41598-021-94266-6.
4
A machine learning framework for multi-hazards modeling and mapping in a mountainous area.用于山区多灾害建模和制图的机器学习框架。
Sci Rep. 2020 Jul 22;10(1):12144. doi: 10.1038/s41598-020-69233-2.
5
Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.基于机器学习模型的降雨诱发滑坡预测:以卢旺达恩戈罗恩戈罗区为例。
Int J Environ Res Public Health. 2020 Jun 10;17(11):4147. doi: 10.3390/ijerph17114147.
6
Assessing and mapping multi-hazard risk susceptibility using a machine learning technique.采用机器学习技术评估和绘制多灾害风险易感性图。
Sci Rep. 2020 Feb 21;10(1):3203. doi: 10.1038/s41598-020-60191-3.
7
Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.机器学习方法在澳大利亚昆士兰州东南部农业干旱的空间建模中的应用。
Sci Total Environ. 2020 Jan 10;699:134230. doi: 10.1016/j.scitotenv.2019.134230. Epub 2019 Sep 6.
8
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9
A global multi-hazard risk analysis of road and railway infrastructure assets.道路和铁路基础设施资产的全球多灾种风险分析。
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