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Landslide hazard assessment of an urban agglomeration in central Guizhou Province based on an information value method and SVM, bagging, DNN algorithm.
Sci Rep
January 2025
Guizhou Provincial Institute of Mountain Resources, No.1 Shaanxi Road, Yunyan District, Guiyang City, Guizhou Province, China.
The urban agglomeration in central Guizhou is located in a crustal deformation area caused by tectonic uplift between the Mesozoic orogenic belt of East Asia and the Alpine-Tethys Cenozoic orogenic belt, with high mountains, steep slopes, fractured rock masses and a fragile ecological environment; this area is the most affected by landslides in Guizhou Province, China. In the past decade, there were a total of 613 medium and large landslide disasters, resulting in 137 deaths and a direct economic loss of 1.032 billion yuan.
View Article and Find Full Text PDFSpatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake.
Sci Total Environ
January 2025
HEOA - West China Health & Medical Geography Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610044, China; Institute for Healthy Cities and West China Research Centre for Rural Health Development, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan 610044, China. Electronic address:
To comprehensively assess regional landslide hazards, we propose a geospatial approach that jointly evaluates both the probability of occurrence (susceptibility) and potential destructive power (intensity) within a single framework, overcoming the limitations of previous studies that treated these two disaster scenarios independently. Focusing on the largest landslide event triggered by the Wenchuan earthquake in China, we collected landslide occurrence and count data at the slope unit level, alongside 18 environmental factors, including seismic data. To enable this multi-hazard single-framework evaluation, we employed two Bayesian spatial joint regressions: the spatial shared component model (SSCM) and the spatial shared hyperparameter model (SSHM).
View Article and Find Full Text PDFSediment flow connectivity index data for the Apulia region (Italy): An open-source geodatabase and the innovative CONNECTOSED WebGIS platform.
Data Brief
February 2025
Department of Earth and Geoenvironmental Sciences, University of Bari, 70125 Bari, Italy.
An open-source geodatabase and its associate WebGIS platform (CONNECTOSED) were developed to collect and utilize data for the Sediment Flow Connectivity Index (SfCI) for the Apulia region of southern Italy. Maps depicting sediment mobility and connectivity across the hydrographic basins of the Apulia region were generated and stored in the geodatabase. This geodatabase is organized into folders containing data in TIFF, shapefile, Jpeg and Pdf formats, including input variables (digital elevation model, land cover map, rainfall map, and soil units dataset for each hydrographic basin), classification graphs (ranking of variable values), dimensionless index maps (slope, ruggedness, rainfall, land cover, and soil stability) and key products (maps of sediment mobility, SfCI, and applied SfCI).
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 PDFInSAR-YOLOv8 for wide-area landslide detection in InSAR measurements.
Sci Rep
January 2025
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.
InSAR monitoring technology is widely used in investigating landslide hazards. Leveraging object detection algorithms to quickly extract landslide information from Wide-Area InSAR measurements is of great significance. Our InSAR-YOLOv8, an algorithm that automatically detects landslides from InSAR measurements, addresses the low accuracy and suboptimal detection performance of existing network models.
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