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        <datestamp>2026-09-12T06:42:45Z</datestamp>
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          <dc:title>Spatial pattern and driving mechanisms of geohazards in the lower yarlung zangbo suture zone: implications for engineering construction in extreme mountainous environments</dc:title>
          <dc:creator>Weiyan Luo (18283825)</dc:creator>
          <dc:creator>Junmeng Zhao (24823920)</dc:creator>
          <dc:creator>Chongyang Bu (24823923)</dc:creator>
          <dc:creator>Kangcheng Zhu (24823926)</dc:creator>
          <dc:creator>Yang Pu (5330972)</dc:creator>
          <dc:creator>Luqi Wang (94435)</dc:creator>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Ecology</dc:subject>
          <dc:subject>Sociology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Lower yarlung zangbo suture zone</dc:subject>
          <dc:subject>geohazard susceptibility</dc:subject>
          <dc:subject>high-altitude long-runout hazards</dc:subject>
          <dc:subject>PCA-LR hybrid model</dc:subject>
          <dc:subject>slope aspect effect</dc:subject>
          <dc:description>&lt;p&gt;The lower Yarlung Zangbo Suture Zone represents an extreme geohazard environment driven by intense tectonic activity and monsoonal hydrology. This study established a database of 492 hazard points (2013–2022) and developed a PCA-LR hybrid model to address multicollinearity and subjectivity in susceptibility assessment. Principal Component Analysis (PCA) was used for dimensionality reduction, followed by Logistic Regression (LR) for sensitivity-based weight refinement.Results reveal significant spatial clustering. Notably, 29.5% of hazards occur on gentle valley slopes (0°–13°), identifying these as accumulation zones for high-altitude long-runout events. Although slope aspect showed low variance in PCA, it demonstrated high sensitivity in the LR model; hazard density on windward slopes is 2.3 times higher than on leeward slopes, highlighting critical microclimatic effects. Furthermore, 83% of hazards cluster within 1,000 m of road networks due to the coupling of unloading fractures and fluvial erosion. The model achieved robust predictive performance (mean AUC = 0.87 ± 0.03 under 5-fold spatial cross-validation). We recommend “avoidance-first” strategies, such as substituting roads with tunnels in highly sensitive segments, to ensure the long-term stability of strategic infrastructure in this extreme mountainous environment.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-12T06:42:45Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.33653100.v1</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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