A Novel Framework for Spatiotemporal Susceptibility Prediction of Rainfall-Induced Landslides: A Case Study in Western Pennsylvania

被引:0
作者
Xiong, Jun [1 ]
Pei, Te [2 ]
Qiu, Tong [3 ]
机构
[1] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA
[2] CUNY City Coll, Dept Civil Engn, New York, NY 10031 USA
[3] Univ Utah, Dept Civil & Environm Engn, Salt Lake City, UT 84112 USA
关键词
landslides; landslide susceptibility mapping; machine learning; rainfall; spatiotemporal prediction; LOGISTIC-REGRESSION; NEURAL-NETWORKS; THRESHOLDS; PROBABILITY; INITIATION; RATIO; MAPS;
D O I
10.3390/rs16183526
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given area. However, due to the rarity of multi-temporal landslide inventory, conventional data-driven LSMs are primarily generated by spatial causative factors, while the temporal factors remain limited. In this study, a spatiotemporal LSM is carried out using machine learning (ML) techniques to assess rainfall-induced landslide susceptibility. To achieve this, two landslide inventories are collected for southwestern Pennsylvania: a spatial inventory and a multi-temporal inventory, with 4543 and 223 historical landslide samples, respectively. The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution. A novel paradigm of data augmentation through non-landslide sampling based on domain knowledge is applied to leverage both spatial and temporal information for ML modeling. The results show that the spatiotemporal ML model using the proposed data augmentation predicts well rainfall-induced landslides in space and time across the study area, with a value of 0.86 of the area under the receiver operating characteristic curve (AUC), which makes it an effective tool in rainfall-induced landslide hazard mitigation and forecasting.
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页数:20
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  • [1] Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
    Ado, Moziihrii
    Amitab, Khwairakpam
    Maji, Arnab Kumar
    Jasinska, Elzbieta
    Gono, Radomir
    Leonowicz, Zbigniew
    Jasinski, Michal
    [J]. REMOTE SENSING, 2022, 14 (13)
  • [2] A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey
    Akgun, Aykut
    [J]. LANDSLIDES, 2012, 9 (01) : 93 - 106
  • [3] Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review
    Akosah, Stephen
    Gratchev, Ivan
    Kim, Dong-Hyun
    Ohn, Syng-Yup
    [J]. REMOTE SENSING, 2024, 16 (16)
  • [4] The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
    Ayalew, L
    Yamagishi, H
    [J]. GEOMORPHOLOGY, 2005, 65 (1-2) : 15 - 31
  • [5] Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy
    Ballabio, Cristiano
    Sterlacchini, Simone
    [J]. MATHEMATICAL GEOSCIENCES, 2012, 44 (01) : 47 - 70
  • [6] Effects of groundwater table position, soil strength properties and rainfall on instability of earthquake-triggered landslides
    Beyabanaki, S. Amir Reza
    Bagtzoglou, Amvrossios C.
    Anagnostou, Emmanouil N.
    [J]. ENVIRONMENTAL EARTH SCIENCES, 2016, 75 (04) : 1 - 13
  • [7] Engineering geology maps:: landslides and geographical information systems
    Chacon, J.
    Irigaray, C.
    Fernandez, T.
    El Hamdouni, R.
    [J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2006, 65 (04) : 341 - 411
  • [8] Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
  • [9] Recommendations for the quantitative analysis of landslide risk
    Corominas, J.
    van Westen, C.
    Frattini, P.
    Cascini, L.
    Malet, J. -P.
    Fotopoulou, S.
    Catani, F.
    Van Den Eeckhaut, M.
    Mavrouli, O.
    Agliardi, F.
    Pitilakis, K.
    Winter, M. G.
    Pastor, M.
    Ferlisi, S.
    Tofani, V.
    Hervas, J.
    Smith, J. T.
    [J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2014, 73 (02) : 209 - 263
  • [10] Crozier M.J., 1986, Landslides: Causes, Consequences and Environment, P171