Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping

Rapid and effective evaluation of landslide susceptibility after earthquakes is critical for various applications, such as emergency rescue, land planning, and disaster prevention. Current research suffers from the lack of a complete landslide inventory and sample selection uncertainty issues. To solve these problems, this study presents a landslide susceptibility mapping model that integrates one-class support vector machine (OCSVM) and an incomplete landslide inventory, which was established with the aid of change detection from bi-temporal Landsat images. Wenchuan County is selected as the study area to test the performance of the proposed method. The proposed method is also compared with standard two-class SVM that selects a sample randomly. Experimental results show that OCSVM can achieve better performance than SVM when only an incomplete landslide inventory is available. The findings of this study can be applied to determine regional landslide susceptibility after earthquakes and provide an essential reference for emergency response.

[1]  Biswajeet Pradhan,et al.  A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) , 2016, Environ. Model. Softw..

[2]  Meei-Ling Lin,et al.  Development of shallow seismic landslide potential map based on Newmark’s displacement: the case study of Chi-Chi earthquake, Taiwan , 2010 .

[3]  Honglin He,et al.  Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China , 2013, Natural Hazards.

[4]  S. Mandal,et al.  Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model , 2018, Applied Geomatics.

[5]  A. Erener,et al.  Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? , 2012, Environmental Earth Sciences.

[6]  New Evaluation Models of Newmark Displacement for Southwest China , 2018, Bulletin of the Seismological Society of America.

[7]  Matthias Vanmaercke,et al.  A data-based landslide susceptibility map of Africa , 2018, Earth-Science Reviews.

[8]  Huifang Li,et al.  A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment , 2018, Earth Science Informatics.

[9]  Qiuming Gong,et al.  Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake, China , 2011 .

[10]  Hao-Yu Tsai,et al.  Slope unit-based approach for assessing regional seismic landslide displacement for deep and shallow failure , 2019, Engineering Geology.

[11]  Weile Li,et al.  Analysis of the geo-hazards triggered by the 12 May 2008 Wenchuan Earthquake, China , 2009 .

[12]  E. Harp,et al.  A method for producing digital probabilistic seismic landslide hazard maps , 2000 .

[13]  Rocío N. Ramos-Bernal,et al.  Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery , 2018, Remote. Sens..

[15]  Wenzhong Shi,et al.  Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[17]  B. Pradhan,et al.  Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China) , 2017 .

[18]  B. Pradhan,et al.  Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran , 2013, Journal of Earth System Science.

[19]  E. Rathje,et al.  The influence of different simplified sliding-block models and input parameters on regional predictions of seismic landslides triggered by the Northridge earthquake , 2013 .

[20]  F. Smedt,et al.  Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal , 2012, Natural Hazards.

[21]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[22]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[23]  S. Oliveira,et al.  Mapping landslide susceptibility using data-driven methods. , 2017, Science of the Total Environment.

[24]  Saro Lee,et al.  Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea , 2018 .

[25]  Huichan Chai,et al.  Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China , 2016, Environmental Earth Sciences.

[26]  L. Luzi,et al.  The application of predictive modeling techniques to landslides induced by earthquakes: the case study of the 26 September 1997 Umbria-Marche earthquake (Italy) , 2003 .

[27]  Johan Spross,et al.  Landslide susceptibility hazard map in southwest Sweden using artificial neural network , 2019 .

[28]  Chong Xu,et al.  Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: a case study of the 2013 Lushan, China, Mw6.6 earthquake , 2018, Natural Hazards.

[29]  Wenzhong Shi,et al.  Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method , 2016 .

[30]  Thomas Oommen,et al.  A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Ainong Li,et al.  Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  C. Westen,et al.  Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake , 2010 .

[33]  Qigen Lin,et al.  Occurrence probability assessment of earthquake-triggered landslides with Newmark displacement values and logistic regression: The Wenchuan earthquake, China , 2016 .

[34]  Jon Atli Benediktsson,et al.  Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Rui Wang,et al.  Regional Landslide Identification Based on Susceptibility Analysis and Change Detection , 2018, ISPRS Int. J. Geo Inf..

[36]  M. Shinoda,et al.  Regional landslide susceptibility following the 2016 Kumamoto earthquake using back-calculated geomaterial strength parameters , 2019, Landslides.

[37]  Chong Xu,et al.  Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis , 2014, Landslides.

[38]  Wanchang Zhang,et al.  GIS-based earthquake-triggered landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China , 2019 .

[39]  J. Godt,et al.  Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal , 2017 .

[40]  K. Yin,et al.  Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine , 2017 .

[41]  Weiwei Zhan,et al.  Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake (Sichuan, China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification , 2018, Landslides.

[42]  Bo Yu,et al.  A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal , 2018, Landslides.

[43]  C. J. van Westen,et al.  Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories , 2012 .

[44]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[45]  Lamek Nahayo,et al.  Comparison of analytical hierarchy process and certain factor models in landslide susceptibility mapping in Rwanda , 2019, Modeling Earth Systems and Environment.