Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.

[1]  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.

[2]  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..

[3]  Kyriaki Papadopoulou-Vrynioti,et al.  Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area , 2013 .

[4]  Y. Galanti,et al.  The influence of geological–morphological and land use settings on shallow landslides in the Pogliaschina T. basin (northern Apennines, Italy) , 2017 .

[5]  Alexander Brenning,et al.  Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province , 2014 .

[6]  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 .

[7]  Saro Lee,et al.  Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea , 2002 .

[8]  K. N. Hjerdt,et al.  A new topographic index to quantify downslope controls on local drainage , 2004 .

[9]  Hamid Reza Pourghasemi,et al.  Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia , 2016, Landslides.

[10]  Saro Lee,et al.  Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea , 2014 .

[11]  Thomas Glade,et al.  The influence of forest cover on landslide occurrence explored with spatio-temporal information , 2017 .

[12]  Tao Chen,et al.  Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China , 2017, Remote. Sens..

[13]  Wei Chen,et al.  A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping , 2017 .

[14]  Anita Simic,et al.  Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio , 2016, Landslides.

[15]  Q. Bao,et al.  Intraseasonal responses of the East Asia summer rainfall to anthropogenic aerosol climate forcing , 2018, Climate Dynamics.

[16]  I. Ilia,et al.  Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece , 2016, Landslides.

[17]  Lin Yan,et al.  Landslide mapping from aerial photographs using change detection-based Markov random field , 2016 .

[18]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[19]  R. Hastie.  Problems for judgment and decision making. , 2001, Annual review of psychology.

[20]  A. Kornejady,et al.  Landslide susceptibility assessment using maximum entropy model with two different data sampling methods , 2017 .

[21]  R. J. Pike,et al.  Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature , 2007 .

[22]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia , 2015, Landslides.

[23]  Biswajeet Pradhan,et al.  Suitability estimation for urban development using multi-hazard assessment map. , 2017, The Science of the total environment.

[24]  G. R. Foster,et al.  Evaluating Irregular Slopes for Soil Loss Prediction , 1974 .

[25]  Saro Lee.  Landslide hazard assessment at Cameron Highland, Malaysia using frequency ratio and logistic regression models , 2006 .

[26]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran , 2017, Environmental Earth Sciences.

[27]  Davide Notti,et al.  The combined use of PSInSAR and UAV photogrammetry techniques for the analysis of the kinematics of a coastal landslide affecting an urban area (SE Spain) , 2017, Landslides.

[28]  Nicolas Huck,et al.  Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , 2017, Eur. J. Oper. Res..

[29]  Jean-Philippe Malet,et al.  Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier , 2017 .

[30]  Saro Lee,et al.  A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea , 2017 .

[31]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

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

[33]  Michael J. Olsen,et al.  A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity , 2017, Landslides.

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

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

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

[37]  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 .

[38]  A. Goudie,et al.  A preliminary geomorphological assessment of part of the Karakoram Highway , 1983, Quarterly Journal of Engineering Geology.

[39]  Iman Nasiri Aghdam,et al.  Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran) , 2016, Environmental Earth Sciences.

[40]  Laura Longoni,et al.  Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives , 2014, Remote. Sens..

[41]  Rainfall Statistics Change in West Bengal (India) from Period 1901-2000 , 2016 .

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

[43]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[44]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[45]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models , 2016 .

[46]  Michele Manunta,et al.  Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys , 2016, Remote. Sens..

[47]  Manfred F. Buchroithner,et al.  Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model , 2011 .

[48]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[49]  Yalin Lei,et al.  The influence of land urbanization on landslides: An empirical estimation based on Chinese provincial panel data. , 2017, The Science of the total environment.

[50]  Takashi Oguchi,et al.  Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest , 2016 .

[51]  J. Gonçalves,et al.  UAV photogrammetry for topographic monitoring of coastal areas , 2015 .

[52]  Lee Saro,et al.  Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model , 2014 .

[53]  Saro Lee,et al.  Statistical analysis of landslide susceptibility at Yongin, Korea , 2001 .

[54]  J. Böhner,et al.  Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation , 2006 .

[55]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[56]  F. Visser,et al.  Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry , 2015 .

[57]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[58]  Cred Unisdr,et al.  The Human Cost Of Natural Disasters: A global perspective , 2015 .

[59]  Saro Lee,et al.  Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea , 2004 .

[60]  Varun Joshi,et al.  Landslide movement monitoring using GPS technology: A case study of Bakthang landslide, Gangtok, East Sikkim, India , 2011 .

[61]  Chun Liu,et al.  Assessment of Regional Shallow Landslide Stability Based on Airborne Laser Scanning Data in the Yingxiu Area of Sichuan Province (China) , 2016 .

[62]  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 .

[63]  Kalliopi Gaki-Papanastassiou,et al.  Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters , 2012, Environmental Earth Sciences.

[64]  S. Schneider,et al.  Climate Change 2001: Synthesis Report: A contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change , 2001 .

[65]  C. J. Westen,et al.  Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models , 2003 .

[66]  Thomas Blaschke,et al.  Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping , 2017, Arabian Journal of Geosciences.

[67]  B. Pradhan,et al.  Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya , 2014, Arabian Journal of Geosciences.

[68]  Joong-Sun Won,et al.  Landslide hazard mapping considering rainfall probability in Inje, Korea , 2016 .

[69]  P. Allemand,et al.  Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion , 2015 .

[70]  Aminaton Marto,et al.  Effect of magnesium chloride solution on the physico-chemical characteristics of tropical peat , 2016, Environmental Earth Sciences.

[71]  P. Borrelli,et al.  A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water , 2015 .

[72]  Zhengang Wang,et al.  Erosion, deposition and soil carbon: A review of process-level controls, experimental tools and models to address C cycling in dynamic landscapes , 2016 .

[73]  S. Weiss,et al.  GLM versus CCA spatial modeling of plant species distribution , 1999, Plant Ecology.

[74]  Jin Kwan Kim,et al.  Effects of soil depth and subsurface flow along the subsurface topography on shallow landslide predictions at the site of a small granitic hillslope , 2016 .

[75]  Bayes Ahmed,et al.  Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh , 2017, Remote. Sens..

[76]  Dieu Tien Bui,et al.  Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS , 2017 .

[77]  Akansha Singh,et al.  Detection of 2011 Sikkim earthquake-induced landslides using neuro-fuzzy classifier and digital elevation model , 2016, Natural Hazards.

[78]  H. Pourghasemi,et al.  Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .