Mapping the landslide susceptibility in Lantau Island, Hong Kong, by frequency ratio and logistic regression model

Landslides are one of the most destructive disasters that cause damage to both property and life every year. The mountainous terrain, heavy and prolonged rainfall and dense development near steep hillsides make Hong Kong as one of the most vulnerable metropolitans to the risk of landslides. Therefore, regional-specific landslide susceptibility assessment in Hong Kong is necessary for hazard management and effective land use planning. Various methodologies have been proposed for landslide susceptibility mappings. By applying the statistically based frequency ratio and logistic regression models in Lantau Island, Hong Kong, this study attempts to add extra value to the literature of evaluating their ‘prediction rate’ (rather than ‘success rate’) for landslide susceptibility mapping in a temporal context. The spatial relationship among landslide occurrence and nine causative factors (elevation, slope, aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to river, distance to fault and lithology) were examined, and landslide susceptibility maps were generated by frequency ratio and logistic regression model. Validations of the mapping results were performed by calculating relative operating characteristics (ROC). Virtually all previous studies randomly extracted and reserved a portion of historical landslide records to perform the model evaluation. In contrast, in this study, 1864 (70%) landslide records in the Enhanced Natural Terrain Landslide Inventory from 2000 to 2008 are utilized to train the model, while the subsequent 799 (30%) landslides occurred from 2008 to 2009 are used for model validation. The validation result shows that logistic regression model (84.05%) possesses a better prediction power than frequency ratio model (76.64%) for the study area. This study attempts to offer a new model assessment strategy for landslide susceptibility mapping. The resultant maps also provide a scientific assessment of the risk areas with respect to landslides on Lantau Island and could serve as a basis for decisions or justification of the Lantau development planning.

[1]  Soyoung Park,et al.  Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea , 2013, Environmental Earth Sciences.

[2]  Chaowei Phil Yang,et al.  Redefining the possibility of digital Earth and geosciences with spatial cloud computing , 2013, Int. J. Digit. Earth.

[3]  E. Shimizu.  Satellite Remote Sensing , 2019, Dictionary of Geotourism.

[4]  Manoj K. Arora,et al.  A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas , 2006 .

[5]  Chong Xu,et al.  GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China , 2012 .

[6]  J. Zêzere,et al.  Landslide Susceptibility Assessment and Validation in the Framework of Municipal Planning in Portugal: The Case of Loures Municipality , 2012, Environmental Management.

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

[8]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[9]  Manoj K. Arora,et al.  Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas , 2008 .

[10]  J. Nichol,et al.  Satellite remote sensing for detailed landslide inventories using change detection and image fusion , 2005 .

[11]  Haijun Wang,et al.  Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China , 2012 .

[12]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[13]  Mukta Sharma,et al.  Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[14]  S. L. Kuriakose,et al.  Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview , 2008 .

[15]  W. Z. Savage,et al.  Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning , 2008 .

[16]  Qiming Zhou,et al.  Utilizing Cloud-Computation to Analyze the Causative Factors of Rainfall-Induced Landslide , 2013, GRMSE.

[17]  S. Reis,et al.  A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics , 2011 .

[18]  Steve Parry,et al.  The Application of Geomorphological Mapping in the Assessment of Landslide Hazard in Hong Kong , 2011 .

[19]  Xueling Wu,et al.  Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China , 2014 .

[20]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[21]  Xu Weiya,et al.  GIS-based landslide hazard assessment: an overview , 2005 .

[22]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[23]  S. Pascale,et al.  Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy) , 2014 .

[24]  Manfred F. Buchroithner,et al.  A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses , 2010, Comput. Environ. Urban Syst..

[25]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[26]  F. Ren,et al.  Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China , 2014, Environmental Earth Sciences.

[27]  Evon M. O. Abu-Taieh,et al.  Comparative Study , 2020, Definitions.

[28]  Murat Ercanoglu,et al.  Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey) , 2011 .

[29]  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 .

[30]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[31]  B. Pradhan,et al.  Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey) , 2012, Environmental Monitoring and Assessment.

[32]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[33]  Saro Lee,et al.  Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping , 2012 .

[34]  Riheb Hadji,et al.  Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: A case study of Souk Ahras region, NE Algeria , 2013 .

[35]  Rosa Lasaponara,et al.  Satellite Remote Sensing: A New Tool for Archaeology , 2012 .

[36]  Jordi Corominas,et al.  A review of assessing landslide frequency for hazard zoning purposes , 2008 .

[37]  Hyun-Joo Oh,et al.  Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis , 2012 .

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

[39]  Paul W. Cleary,et al.  Combining digital terrain and surface textures with large-scale particle-based computational models to predict dam collapse and landslide events , 2010 .

[40]  P. Quinn.  Landslide susceptibility in sensitive clay in eastern Canada: some practical considerations and results in development of an improved model , 2014 .

[41]  M. Diederichs,et al.  Regional-scale landslide susceptibility mapping using the weights of evidence method: an example applied to linear infrastructure , 2010 .

[42]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[43]  David Alexander,et al.  A brief survey of GIS in mass-movement studies, with reflections on theory and methods , 2008 .

[44]  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..

[45]  S. Lee,et al.  Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea , 2007 .

[46]  Joong-Sun Won,et al.  The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea , 2006 .

[47]  Ashish Pandey,et al.  Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India , 2008 .

[48]  C. F. Lee,et al.  On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong , 2002 .

[49]  J. Klimeš.  Landslide temporal analysis and susceptibility assessment as bases for landslide mitigation, Machu Picchu, Peru , 2013, Environmental Earth Sciences.

[50]  Saro Lee,et al.  Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS , 2012 .

[51]  P. Reichenbach,et al.  Probabilistic landslide hazard assessment at the basin scale , 2005 .

[52]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[53]  Benni Thiebes,et al.  Landslide Analysis and Early Warning Systems: Local and Regional Case Study in the Swabian Alb, Germany , 2012 .

[54]  Yang Hong,et al.  Towards an early‐warning system for global landslides triggered by rainfall and earthquake , 2007 .

[55]  N. Intarawichian,et al.  Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand , 2011 .

[56]  Pradhan Biswajeet,et al.  Utilization of Optical Remote Sensing Data and GIS Tools for Regional Landslide Hazard Analysis Using an Artificial Neural Network Model , 2007 .

[57]  Chiara Lepore,et al.  Rainfall-induced landslide susceptibility zonation of Puerto Rico , 2012, Environmental Earth Sciences.

[58]  Fuchu Dai,et al.  Landslide risk assessment and management: an overview , 2002 .

[59]  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..

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

[61]  Saro Lee,et al.  Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique , 2013, Environmental Earth Sciences.

[62]  H. Shahabi,et al.  Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models , 2014 .

[63]  Chong Xu,et al.  The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling , 2012, Environmental Earth Sciences.

[64]  Andrea G. Fabbri,et al.  Validation of Spatial Prediction Models for Landslide Hazard Mapping , 2003 .

[65]  B. Pradhan,et al.  Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models , 2010 .

[66]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[67]  John P. Wilson,et al.  Terrain analysis : principles and applications , 2000 .

[68]  K. T. Chau,et al.  Landslide hazard analysis for Hong Kong using landslide inventory and GIS , 2004, Comput. Geosci..

[69]  Christian Conoscenti,et al.  GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy) , 2008 .

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

[71]  Manfred F. Buchroithner,et al.  Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia) , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[72]  D. Cruden.  A simple definition of a landslide , 1991 .

[73]  Chandra Prakash Poudyal,et al.  Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya , 2010 .

[74]  Hyun-Joo Oh,et al.  Quantitative landslide susceptibility mapping at Pemalang area, Indonesia , 2010 .

[75]  J. Hutchinson,et al.  Hillslope Form and Process , 1973 .

[76]  Michael F. Goodchild,et al.  Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? , 2011, Int. J. Digit. Earth.

[77]  C. Gokceoğlu,et al.  Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques , 1996 .

[78]  L. Hurni,et al.  Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments , 2005 .

[79]  A. Yalçın.  GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations , 2008 .

[80]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[81]  D. Varnes.  Landslide hazard zonation: A review of principles and practice , 1984 .

[82]  Karim Solaimani,et al.  Landslide susceptibility mapping based on frequency ratio and logistic regression models , 2013, Arabian Journal of Geosciences.

[83]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[84]  Lewis A. Owen,et al.  GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region , 2008 .

[85]  T. Kavzoglu,et al.  Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression , 2014, Landslides.

[86]  Saro Lee,et al.  Validation of an artificial neural network model for landslide susceptibility mapping , 2010 .

[87]  F. Mancini,et al.  GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy , 2010 .

[88]  Saro Lee.  Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS , 2004, Environmental management.

[89]  Giovanni B. Crosta,et al.  Rainfall‐induced landslides and debris flows , 2008 .

[90]  Wang Sijing,et al.  Analysis of rainstorm-induced slide-debris flows on natural terrain of Lantau Island, Hong Kong , 1999 .

[91]  A. Akgun.  A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey , 2012, Landslides.

[92]  Biswajeet Pradhan,et al.  Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia , 2011 .