Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

被引:3
作者
Heo, Sujung [1 ]
Sohn, Wonmin [2 ]
Park, Sangjin [3 ]
Lee, Dong Kun [4 ,5 ]
机构
[1] Seoul Natl Univ, Interdisciplinary Program Landscape Architecture, Seoul, South Korea
[2] Michigan State Univ, Sch Planning Design & Construct, E Lansing, MI USA
[3] Korea Inst Publ Adm, Seoul, South Korea
[4] Seoul Natl Univ, Dept Landscape Architecture & Rural Syst Engn, Seoul, South Korea
[5] Seoul Natl Univ, Res Inst Agr & Life Sci, Seoul, South Korea
关键词
Natural hazard; Machine learning algorithm; Risk mitigation; Protected area; Nusantara; Kalimantan; Sumatra; Indonesia; SUPPORT VECTOR MACHINE; RANDOM FOREST; SUSCEPTIBILITY ASSESSMENT; SPATIAL PREDICTION; MODEL; CLASSIFICATION; IMPACTS; ALGORITHMS; SYSTEM; TREE;
D O I
10.1016/j.heliyon.2024.e37789
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models-na & iuml;ve Bayes, k-nearest neighbors, and random forest (RF)-was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.
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页数:17
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