Natural Hazards Review

Landslide disasters have inflicted incalculable losses on China’s national economy, as well as on lives and property. Notably, 90% of landslide disasters are directly induced by rainfall or have indirect associations with it. In Bazhong City, Sichuan Province, China, the proportion of rainfall-induced landslides accounts for more than 70% of all geological disasters in the region. Our research undertook a susceptibility analysis of multimodal landslide data in Bazhou District of Bazhong City, employing four distinct machine learning methods: decision trees (DTs), random forests (RFs), support vector machines (SVMs), and back-propagation neural networks (BPNNs). Additionally, data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation product were utilized to develop a rainfall intensity-duration (I-D) model for the Bazhou District. The experimental results indicated that the BPNN achieved the highest overall classification accuracy, reaching 92.00%, which was 3.00% to 6.00% higher than those achieved by other algorithms. The kappa coefficient for BPNN was 0.84, surpassing other algorithms by 0.06 to 0.10. Furthermore, our results demonstrated that the rainfall I-D model had a prediction accuracy of 90.91% for rainfall-induced landslides. Finally, a probability quantification model for landslide triggering factors was established based on the previous two research results, aimed at meteorological warning. Comparisons with five recorded landslide events in 2009 revealed that the experimental outcomes of the meteorological early warning model aligned with the actual inspection results. Therefore, this model can serve as a reliable reference for issuing warnings about rainfall-induced landslides in Bazhou District.

Communities need to prepare for anticipated hazards, adapt to varying conditions, and resist and recover rapidly from disturbances. Protecting the built environment from natural and human-made hazards and understanding the impact of these hazards helps allocate resources efficiently. Recently, an indicator-based and time-dependent approach was developed for continuously defining and measuring the functionality and disaster resilience at the community level. The PEOPLES framework consists of seven dimensions (population and demographics, environmental and ecosystem, organized governmental services, physical infrastructure, lifestyle and community competence, economic development, and social-cultural capital), and the indicator-based approach finds qualitative characteristics and transforms them into quantitative measures. The proposed framework is used to study the resilience of rural communities subject to flood hazards. Harlan County, Kentucky, in the US Appalachian region is chosen as a case study to evaluate the proposed resilience quantification framework subject to severe flooding. The results show the validity of the proposed approach as a decision-support mechanism to assess and enhance the resilience of rural communities. The novelty of this case study paper is threefold: (1) a holistic indicator-based resilience quantification framework is used, (2) the aim of this study is focused on rural communities, and (3) it offers a unified way of addressing the effects of flood hazards.