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

Abstract

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—naïve 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.

Keywords

• Natural hazard
• Machine learning algorithm
• Risk mitigation
• Protected area
• Nusantara
• Kalimantan