Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

ABSTRACT The main objective of this study is to evaluate the performances of different earthquake-induced landslides susceptibility mapping models at mountainous regions in China. At first, 160 earthquake-induced landslide points were identified from field investigations. Concurrently, based on the results of a literature review and the field investigation, 12 influencing factors were considered, and the corresponding thematic layers were generated using geographic information system (GIS) technology. Subsequently, 20 groups with a fixed number of cells were collected as a common training dataset for the two different models, based on a random selection from the entire database (including landslide cells and no-landslide cells). The neural network (NN) model and logistic regression (LR) model were developed with R software. Finally, earthquake-induced landslides susceptibility maps of Wenchuan county were produced, very low, low, medium, high and very high susceptibility zones cover. The validation results indicate that the landslide data from field investigations are in good agreement with the evaluation results, and the LR model has a slightly better prediction than the NN model in this case. In general, the NN model and LR models are satisfactory for susceptibility mapping of earthquake-induced landslides at mountainous regions.

[1]  Chong Xu,et al.  Statistical analysis of landslides caused by the Mw 6.9 Yushu, China, earthquake of April 14, 2010 , 2014, Natural Hazards.

[2]  C. F. Lee,et al.  Logistic regression modelling of storm-induced shallow landsliding in time and space on natural terrain of Lantau Island, Hong Kong , 2004 .

[3]  Saro Lee,et al.  Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data , 2005 .

[4]  Gokhan Saygili,et al.  Empirical Predictive Models for Earthquake-Induced Sliding Displacements of Slopes , 2008 .

[5]  Masahiro Shinoda,et al.  Regional landslide susceptibility following the Mid NIIGATA prefecture earthquake in 2004 with NEWMARK’S sliding block analysis , 2017, Landslides.

[6]  José M. Paruelo,et al.  Two decades of Normalized Difference Vegetation Index changes in South America: identifying the imprint of global change , 2004 .

[7]  Ping Sun,et al.  Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China , 2009 .

[8]  R. Dahal,et al.  Rainfall event-based landslide susceptibility zonation mapping , 2013, Natural Hazards.

[9]  D. V. Griffiths,et al.  Evaluation of landslide mechanisms characterized by high-speed mass ejection and long-run-out based on events following the Wenchuan earthquake , 2015 .

[10]  Antonio Francipane,et al.  Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a Sicilian catchment , 2014 .

[11]  Athanassios Ganas,et al.  Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece , 2014 .

[12]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[13]  Wenjun Zheng,et al.  Landslides triggered by the 22 July 2013 Minxian–Zhangxian, China, Mw 5.9 earthquake: Inventory compiling and spatial distribution analysis , 2014 .

[14]  Qiang Xu,et al.  Rapid susceptibility mapping of co-seismic landslides triggered by the 2013 Lushan Earthquake using the regression model developed for the 2008 Wenchuan Earthquake , 2013, Journal of Mountain Science.

[15]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

[16]  Fabio Matano,et al.  An integrated approach to earthquake-induced landslide hazard zoning based on probabilistic seismic scenario for Phlegrean Islands (Ischia, Procida and Vivara), Italy , 2017 .

[17]  Meng Liu,et al.  The use of remotely sensed data and ground survey tools to assess damage and monitor early recovery following the 12.5.2008 Wenchuan earthquake in China , 2012, Bulletin of Earthquake Engineering.

[18]  Gaozhong Nie,et al.  Relationships between ground motion parameters and landslides induced by Wenchuan earthquake , 2010 .

[19]  D. Keefer,et al.  Investigating Landslides Caused by Earthquakes – A Historical Review , 2002 .

[20]  Katerina Kavoura,et al.  Assessment of Earthquake-Induced Landslide Hazard in Greece: From Arias Intensity to Spatial Distribution of Slope Resistance Demand , 2016 .

[21]  Mustafa Neamah Jebur,et al.  Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .

[22]  Gonghui Wang,et al.  Study of the 1920 Haiyuan earthquake-induced landslides in loess (China) , 2007 .

[23]  John C. Davis,et al.  Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .

[24]  Mohammad Kazem Jafari,et al.  Landslides triggered by the Avaj, Iran earthquake of June 22, 2002 , 2006 .

[25]  Randall W. Jibson,et al.  PREDICTING EARTHQUAKE-INDUCED LANDSLIDE DISPLACEMENTS USING NEWMARK'S SLIDING BLOCK ANALYSIS , 1993 .

[26]  D. Keefer,et al.  Dynamic analysis of a slope failure from the 6 August 1979 Coyote Lake, California, earthquake , 1983 .

[27]  Chong Xu,et al.  Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, M w = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis , 2014 .

[28]  Qiang Xu,et al.  Co-seismic landslide inventory and susceptibility mapping in the 2008 Wenchuan earthquake disaster area, China , 2013, Journal of Mountain Science.

[29]  Chong Xu,et al.  Optimized volume models of earthquake-triggered landslides , 2016, Scientific Reports.

[30]  Masahiro Chigira,et al.  Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan , 2006 .

[31]  C. Lee,et al.  Empirical estimation of the Newmark displacement from the Arias intensity and critical acceleration , 2011 .

[32]  Andrew D. Weiss.  Topographic position and landforms analysis , 2001 .

[33]  Xiwei Xu,et al.  Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China , 2012, Comput. Geosci..

[34]  Comparison of landslide inventories from the 1994 Mw 6.8 Arthurs Pass and 2015 Mw 6.0 Wilberforce earthquakes, Canterbury, New Zealand , 2017, Landslides.

[35]  D. Keefer.  Statistical analysis of an earthquake-induced landslide distribution — the 1989 Loma Prieta, California event , 2000 .

[36]  Guan-Wei Lin,et al.  Effects of topography, lithology, rainfall and earthquake on landslide and sediment discharge in mountain catchments of southeastern Taiwan , 2011 .

[37]  C. Lee.  Re-Evaluation of Factors Controlling Landslides Triggered by the 1999 Chi–Chi Earthquake , 2013 .

[38]  Scott B. Miles,et al.  Rigorous landslide hazard zonation using Newmark's method and stochastic ground motion simulation , 1999 .

[39]  Haijia Wen.  A susceptibility mapping model of earthquake-triggered slope geohazards based on geo-spatial data in mountainous regions , 2015 .

[40]  S. Bai,et al.  Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression , 2013, Environmental Earth Sciences.

[41]  A. Rajabi,et al.  Attenuation relation of Arias intensity for Zagros Mountains region (Iran) , 2010 .

[42]  Evaluation model of landslide hazards induced by the 2008 Wenchuan earthquake using strong motion data , 2011 .

[43]  E. Ausilio,et al.  An uncoupled procedure for performance assessment of slopes in seismic conditions , 2017, Bulletin of Earthquake Engineering.

[44]  Gang Wang,et al.  A one-step Newmark displacement model for probabilistic seismic slope displacement hazard analysis , 2016 .

[45]  T. Sekiguchi,et al.  Overlaying landslides distribution on the earthquake source, geological and topographical data: the Mid Niigata prefecture earthquake in 2004, Japan , 2005 .

[46]  Liang Bin,et al.  Geohazards Induced by the Lushan Ms7.0 Earthquake in Sichuan Province, Southwest China: Typical Examples, Types and Distributional Characteristics , 2013 .

[47]  Saro Lee,et al.  Earthquake-induced landslide-susceptibility mapping using an artificial neural network , 2006 .

[48]  A. Zini,et al.  Mapping the earthquake-induced landslide hazard around the main oil pipeline network of the Agri Valley (Basilicata, southern Italy) by means of two GIS-based modelling approaches , 2016, Natural Hazards.

[49]  David J. Wald,et al.  Development of a globally applicable model for near real-time prediction of seismically induced landslides , 2014 .

[50]  Chong Xu,et al.  Database and spatial distribution of landslides triggered by the Lushan, China Mw 6.6 earthquake of 20 April 2013 , 2015 .

[51]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[52]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[53]  Chong Xu,et al.  Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian, Gansu, China, Mw 5.9 event , 2017, Landslides.

[54]  Ju-Jiang Hung,et al.  Chi-Chi Earthquake Induced Landslides in Taiwan , 2000 .

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

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

[57]  N. Hovius,et al.  Regional patterns of earthquake‐triggered landslides and their relation to ground motion , 2007 .

[58]  Zhiqiang Yin,et al.  Distribution characteristics of geohazards induced by the Lushan Earthquake and their comparisons with the Wenchuan Earthquake , 2014, Journal of Earth Science.

[59]  Chen Guo,et al.  Size and spatial distribution of landslides induced by the 2015 Gorkha earthquake in the Bhote Koshi river watershed , 2017, Journal of Mountain Science.

[60]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[61]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[62]  M. Cecchini,et al.  Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease , 2016, Scientific Reports.

[63]  Qiang Xu,et al.  Spatial distribution analysis of landslides triggered by 2008.5.12 Wenchuan Earthquake, China , 2010 .

[64]  Empirical-based seismically induced slope displacements in a geographic information system environment: a case study , 2014 .

[65]  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.

[66]  P. Xiao,et al.  Rapid susceptibility mapping of earthquake-triggered slope geohazards in Lushan County by combining remote sensing with the AHP model developed for the Wenchuan earthquake , 2017, Bulletin of Engineering Geology and the Environment.

[67]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[68]  Wang Ai-guo,et al.  Geometric pattern and active tectonics in Southeastern Gansu province: Discussion on seismogenic mechanism of the Minxian-Zhangxian M(S)6. 6 earthquake on July 22, 2013 , 2013 .

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

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

[71]  Wenguang Huang,et al.  Land use change and landslide characteristics analysis for community-based disaster mitigation , 2013, Environmental Monitoring and Assessment.

[72]  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.