Landslides pose a devastating threat to human health, killing thousands of people annually. Human vulnerability is a crucial element of landslide risk reduction, yet up until now, all methods for estimating the human consequences of landslides rely on subjective, expert judgment. Furthermore, these methods do not explore the underlying causes of mortality or inform strategies to reduce landslide risk. In light of these issues, we develop a data-driven tool to estimate an individual's probability of death based on landslide intensity, which can be used directly in landslide risk assessment. We find that between inundation depths of approximately 1-6 m, human behavior is the primary driver of mortality. Landslide vulnerability is strongly correlated with the economic development of a region, but landslide losses are not stratified by gender and age to the degree of other natural hazards. We observe that relatively simple actions, such as moving to an upper floor or a prepared refuge space, increase the odds of survival by up to a factor of 12. Additionally, community-scale hazard awareness programs and training for citizen first responders offer a potent means to maximize survival rates in landslides.
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http://dx.doi.org/10.1029/2020GH000287 | DOI Listing |
Sci Rep
January 2025
Information Institute of the Ministry of Emergency Management of PR China, Beijing, 100029, People's Republic of China.
Slopes influenced by multiple faults are prone to large-scale landslides triggered by multi-regional failures. Understanding the failure process and sequence is essential for the sustainable development of mining operations. This paper presents a method combining InSAR monitoring and numerical simulation to analyze the failure processes of slopes affected by multiple faults.
View Article and Find Full Text PDFThis study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
View Article and Find Full Text PDFSci Rep
January 2025
School of Highway, Chang'an University, Xi'an, 710064, Shaanxi, China.
The long-term safety and durability of anchor systems are the focus of slope maintenance management and sustainable operation. This study presents the observed temperature, humidity, and anchor bolt stress at varying depths from four-year remote real-time monitoring of the selected loess highway cut-slope. The potential correlation between slope hydrothermal environment and anchor stress is analyzed.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Mathematics, University of Bologna, 40126 Bologna, Italy.
Accurate estimation of landslide depth is essential for practical hazard assessment and risk mitigation. This work addresses the problem of determining landslide depth from satellite-derived elevation data. Using the principle of mass conservation, this problem can be formulated as a linear inverse problem.
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