A new landslide inventory and improved susceptibility model for northeastern Pennsylvania

被引:0
|
作者
Karimi B. [1 ]
Yanchuck M. [1 ]
Foust J. [1 ]
机构
[1] Department of Environmental Engineering and Earth Sciences, Wilkes University, Wilkes-Barre, PA
关键词
53;
D O I
10.1306/eg.09191919008
中图分类号
学科分类号
摘要
Landslides are geologic events that cost Pennsylvania $127 million in 2018. Landslide susceptibility models, or maps that depict where landslides are likely to occur, are helpful tools for the public and private sectors to use to mitigate the cost and damage caused by mass movements. However, Pennsylvania's most current statewide susceptibility map for landslides is broad and only suitable for analysis at the state level. The majority of northeastern Pennsylvania (NEPA) falls within a low susceptibility zone, but within this zone are undefined areas of moderate to high susceptibility. This broad range of susceptibility provides no slope-specific description of the moderate to high classifications. Pennsylvania's coarse resolution susceptibility model is likely caused by the lack of a comprehensive landslide inventory for the entire state that might be used in data-driven methods of susceptibility modeling. To create a high-resolution susceptibility map for NEPA, a landslide inventory for NEPA was constructed based on enhanced imagery and analysis of light detection and ranging-derived digital terrain models. A data-driven bivariate frequency ratio method was used for the creation of a 30-m pixel-resolution susceptibility map that is both qualitatively and quantitatively more robust than the most current model within the region. Our results indicate that within NEPA, slope failures are most influenced by the slope derivative of elevation. Slopes are most susceptible to failure along steep valleys created by rivers and streams within the Appalachian Plateau, as well as areas with steep slope within the Ridge and Valley areas of the study region. INTRODUCTION Mass movements, commonly generalized with the term "landslides," can be catastrophic geological events that cost the United States over. Copyright © 2019. The American Association of Petroleum Geologists/Division of Environmental Geosciences. All rights reserved.
引用
收藏
页码:125 / 145
页数:20
相关论文
共 50 条
  • [31] Completeness of landslide inventory and landslide susceptibility mapping using logistic regression method in Ceyhan Watershed (southern Turkey)
    Tekin S.
    Arabian Journal of Geosciences, 2021, 14 (17)
  • [32] Landslide inventory maps: New tools for an old problem
    Guzzetti, Fausto
    Mondini, Alessandro Cesare
    Cardinali, Mauro
    Fiorucci, Federica
    Santangelo, Michele
    Chang, Kang-Tsung
    EARTH-SCIENCE REVIEWS, 2012, 112 (1-2) : 42 - 66
  • [33] The propagation of inventory-based positional errors into statistical landslide susceptibility models
    Steger, Stefan
    Brenning, Alexander
    Bell, Rainer
    Glade, Thomas
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2016, 16 (12) : 2729 - 2745
  • [34] Landslide susceptibility mapping and model performance assessment
    Xiao, Chenchao
    Tian, Yuan
    Si, Kangping
    Li, Ting
    ADVANCES IN CIVIL AND INDUSTRIAL ENGINEERING, PTS 1-4, 2013, 353-356 : 3487 - +
  • [35] AN UPDATE ON THE LANDSLIDE INVENTORY AND SUSCEPTIBILITY MODELLING IN THE ILLAWARRA WITH AN ANALYSIS OF THE 2022 RAINFALL EVENTS
    Larkin, Connor
    Palamakumbure, Darshika
    Flentje, Phil
    AUSTRALIAN GEOMECHANICS JOURNAL, 2024, 59 (03): : 137 - 149
  • [36] Implications of landslide inventory in susceptibility modeling along a Himalayan highway corridor, India
    Pandey, Vijendra Kumar
    Tripathi, Arun Kumar
    Sharma, Kaushal Kumar
    PHYSICAL GEOGRAPHY, 2022, 43 (04) : 440 - 462
  • [37] A NEW PERSPECTIVE FOR REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT
    Titti G.
    Antelmi M.
    Fusco F.
    Longoni L.
    Borgatti L.
    Italian Journal of Engineering Geology and Environment, 2024, (Special Issue 1): : 275 - 283
  • [38] A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors
    Wu, Chun-Yi
    Yeh, Yen-Chu
    WATER, 2020, 12 (04)
  • [39] Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
    Dae-Hong Min
    Hyung-Koo Yoon
    Scientific Reports, 11
  • [40] An Improved Bayesian Classification Data mining Method for Early Warning Landslide Susceptibility Model Using GIS
    Venkatesan, M.
    Thangavelu, Arunkumar
    Prabhavathy, P.
    PROCEEDINGS OF SEVENTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS (BIC-TA 2012), VOL 2, 2013, 202 : 277 - +