Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

[1]  M Stoffel,et al.  Debris-flow risk analysis in a managed torrent based on a stochastic life-cycle performance. , 2016, The Science of the total environment.

[2]  Palash Panja,et al.  Application of artificial intelligence to forecast hydrocarbon production from shales , 2017 .

[3]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines for analysis of geotechnical engineering systems , 2013 .

[4]  David L. George,et al.  A depth-averaged debris-flow model that includes the effects of evolving dilatancy. I. Physical basis , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  M. Jakob,et al.  Debris-flow Hazards and Related Phenomena , 2005 .

[6]  Pascal Horton,et al.  FLOW-R, A MODEL FOR DEBRIS FLOW SUSCEPTIBILITY MAPPING AT A REGIONAL SCALE - SOME CASE STUDIES , 2011 .

[7]  Anshuman Bhardwaj,et al.  LiDAR remote sensing of the cryosphere: Present applications and future prospects , 2016 .

[8]  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 .

[9]  Bingwen Yu,et al.  Development of particle swarm optimization–support vector regression (PSO‐SVR) coupled with microwave plasma torch–atomic emission spectrometry for quality control of ginsengs , 2017 .

[10]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Kirolos Haleem,et al.  Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas. , 2013, Accident; analysis and prevention.

[13]  F. Liébault,et al.  Debris-flow activity in abandoned channels of the Manival torrent reconstructed with LiDAR and tree-ring data , 2011 .

[14]  Biswajeet Pradhan,et al.  Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia , 2011, Expert Syst. Appl..

[15]  Hamid Reza Pourghasemi,et al.  Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion , 2017 .

[16]  Vinod Kumar Jain,et al.  Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification , 2018, Appl. Soft Comput..

[17]  Tao Zhao,et al.  Contribution of Excessive Supply of Solid Material to a Runoff-Generated Debris Flow during Its Routing Along a Gully and Its Impact on the Downstream Village with Blockage Effects , 2019, Water.

[18]  A-Xing Zhu,et al.  Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. , 2018, The Science of the total environment.

[19]  Bahareh Kalantar,et al.  Debris Flow Susceptibility Assessment Using Airborne Laser Scanning Data , 2017 .

[20]  Pijush Samui,et al.  A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus ( G max ) and Minimum Damping Ratio ( min ) , 2013 .

[21]  Francisco Javier de Cos Juez,et al.  Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS) , 2011, Expert Syst. Appl..

[22]  V. Singh,et al.  Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods , 2018, Scientific Reports.

[23]  Biswajeet Pradhan,et al.  Geomorphometric Analysis of Landform Pattern Using Topographic Position and ASTER GDEM , 2017 .

[24]  M. Zare,et al.  Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? , 2018 .

[25]  A. S. Toprak,et al.  An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area , 2015 .

[26]  Stefan Kienzle,et al.  The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives , 2004, Trans. GIS.

[27]  Markus N. Zimmermann,et al.  The 1987 debris flows in Switzerland: documentation and analysis , 1993 .

[28]  Giovanni B. Crosta,et al.  REGIONAL-SCALE DEBRIS-FLOW RISK ASSESSMENT FOR AN ALPINE VALLEY , 2011 .

[29]  Gasing Fiona.  Evaluation of wind hazard over Sabah And Sarawak , 2015 .

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

[31]  Haliza Abdul Rahman.  AN OVERVIEW OF ENVIRONMENTAL DISASTER IN MALAYSIA AND PREPAREDNESS STRATEGIES , 2014 .

[32]  Qinbao Song,et al.  A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data , 2013, IEEE Transactions on Knowledge and Data Engineering.

[33]  Michael Becht,et al.  Using terrestrial LiDAR data to analyse morphodynamics on steep unvegetated slopes driven by different geomorphic processes , 2016 .

[34]  Nicholas C. Coops,et al.  Evaluating error associated with lidar-derived DEM interpolation , 2009, Comput. Geosci..

[35]  Lloyd A. Smith,et al.  Practical feature subset selection for machine learning , 1998 .

[36]  Shovan Lal Chattoraj,et al.  Debris Flow Modelling and Risk Assessment of Selected Landslides from Uttarakhand- Case Studies using Earth Observation Data , 2017 .

[37]  I. Evans,et al.  Elementary forms for land surface segmentation: The theoretical basis of terrain analysis and geomorphological mapping , 2008 .

[38]  Pijush Samui,et al.  A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio , 2012 .

[39]  Michael Krautblatter,et al.  Evidence for enhanced debris-flow activity in the Northern Calcareous Alps since the 1980s (Plansee, Austria) , 2016 .

[40]  A. Large.  Laser Scanning for the Environmental Sciences , 2009 .

[41]  Biswajeet Pradhan,et al.  Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree , 2016, Landslides.

[42]  G. Dalla Fontana,et al.  The triggering of debris flow due to channel‐bed failure in some alpine headwater basins of the Dolomites: analyses of critical runoff , 2008 .

[43]  Lin Ma,et al.  Mining the customer credit using hybrid support vector machine technique , 2009, Expert Syst. Appl..

[44]  Enrico Foti,et al.  A comparative assessment of two different debris flow propagation approaches – blind simulations on a real debris flow event , 2014 .

[45]  Alessandro Simoni,et al.  Atmospheric circulation patterns, cloud-to-ground lightning, and locally intense convective rainfall associated with debris flow initiation in the Dolomite Alps of northeastern Italy , 2015 .

[46]  Hui Chen,et al.  Susceptibility assessment of debris flows using the analytic hierarchy process method − A case study in Subao river valley, China , 2015 .

[47]  Michel Jaboyedoff,et al.  Sediment budget monitoring of debris-flow and bedload transport in the Manival Torrent, SE France , 2012 .

[48]  Berthold K. P. Horn,et al.  Hill shading and the reflectance map , 1981, Proceedings of the IEEE.

[49]  Saro Lee,et al.  Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea , 2017 .

[50]  Bruce R. Kowalski,et al.  MARS: A tutorial , 1992 .

[51]  Mark A. Weltz,et al.  Combining 3D data and traditional soil erosion assessment techniques to study the effect of a vegetation cover gradient on hillslope runoff and soil erosion in a semi-arid catchment , 2018, CATENA.

[52]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[53]  Ping Zhang,et al.  Feature selection by integrating two groups of feature evaluation criteria , 2018, Expert Syst. Appl..

[54]  Donald E. Brown,et al.  Global Optimization With Multivariate Adaptive Regression Splines , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[55]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[56]  T. Hastie,et al.  Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions , 2006 .

[57]  Rinaldo Genevois,et al.  Comparison of numerical models of two debris flows in the Cortina d’ Ampezzo area, Dolomites, Italy , 2008 .

[58]  Simon Fong,et al.  Optimized Swarm Search-Based Feature Selection for Text Mining in Sentiment Analysis , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[59]  Tao Pei,et al.  An approach to computing topographic wetness index based on maximum downslope gradient , 2011, Precision Agriculture.

[60]  Roland Kaitna,et al.  Modeling debris-flow runout patterns on two alpine fans with different dynamic simulation models , 2015 .

[61]  J. Adinarayana,et al.  Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia , 2011, Environmental Monitoring and Assessment.

[62]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[63]  Qiang Xu,et al.  Characteristics and numerical runout modelling of a catastrophic rock avalanche triggered by the Wenchuan earthquake in the Wenjia valley, Mianzhu, Sichuan, China , 2017, Landslides.

[64]  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..

[65]  Claudio Margottini,et al.  Large scale debris-flow hazard assessment: a geotechnical approach and GIS modelling , 2003 .

[66]  Allen Bateman,et al.  Application of FLATModel, a 2D finite volume code, to debris flows in the northeastern part of the Iberian Peninsula , 2008 .

[67]  M. Jaboyedoff,et al.  Use of LIDAR in landslide investigations: a review , 2012, Natural Hazards.

[68]  Charles B. Roosen,et al.  An introduction to multivariate adaptive regression splines , 1995, Statistical methods in medical research.

[69]  Marco Uzielli,et al.  Temporal evolution of flow-like landslide hazard for a road infrastructure in the municipality of Nocera Inferiore (southern Italy) under the effect of climate change , 2018, Natural Hazards and Earth System Sciences.

[70]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[71]  C. Chen,et al.  Debris flow-induced topographic changes: effects of recurrent debris flow initiation , 2017, Environmental Monitoring and Assessment.

[72]  Anthony T. C. Goh,et al.  Multivariate Adaptive Regression Splines Application for Multivariate Geotechnical Problems with Big Data , 2016, Geotechnical and Geological Engineering.

[73]  Hamid Reza Pourghasemi,et al.  Spatial modelling of gully erosion in Mazandaran Province, northern Iran , 2018 .

[74]  Yi Liu,et al.  FS_SFS: A novel feature selection method for support vector machines , 2006, Pattern Recognit..

[75]  Abdul Halim Ghazali,et al.  Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS , 2017 .

[76]  Biswajeet Pradhan,et al.  Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS , 2016 .

[77]  C. Jan,et al.  Characteristics of rainfall triggering of debris flows in the Chenyulan watershed, Taiwan , 2013 .

[78]  Simon D. Jones,et al.  Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques , 2019, CATENA.

[79]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[80]  Biswajeet Pradhan,et al.  Data Fusion Technique Using Wavelet Transform and Taguchi Methods for Automatic Landslide Detection From Airborne Laser Scanning Data and QuickBird Satellite Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[81]  Tamotsu Takahashi,et al.  What is debris flow , 2007 .

[82]  Khamarrul Azahari Razak,et al.  Different methods of landslide mapping in Cameron Highlands , 2015 .

[83]  Yves Bühler,et al.  A robust debris-flow and GLOF risk management strategy for a data-scarce catchment in Santa Teresa, Peru , 2016, Landslides.

[84]  Andrew O. Finley,et al.  Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data , 2016 .

[85]  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 .

[86]  Ali Karouni,et al.  Predicting Forest Fire Hazards Using Data Mining Techniques: Decision Tree and Neural Networks , 2014 .

[87]  Wei Chen,et al.  Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques , 2017, Geomorphology.

[88]  E. Rotigliano,et al.  Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy) , 2014 .

[89]  Justin K. Dix,et al.  Assessing debris flows using LIDAR differencing: 18 May 2005 Matata event, New Zealand , 2010 .

[90]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[91]  B. Pradhan,et al.  Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia , 2010 .

[92]  C. Chen,et al.  Morphometric analysis of debris flows and their source areas using GIS , 2011 .

[93]  Hamid R. Safavi,et al.  Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms , 2013, Water Resources Management.

[94]  Toyohiko Miyagi,et al.  Open image in new windowThe Simulation of a Deep Large-Scale Landslide Near Aratozawa Dam Using a 3.0 MPa Undrained Dynamic Loading Ring Shear Apparatus , 2014 .

[95]  P. Cui,et al.  Method of debris flow prediction based on a numerical weather forecast and its application , 2006 .

[96]  Ronghua Ma,et al.  Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information , 2018, Geomorphology.

[97]  Wengang Zhang,et al.  Multivariate adaptive regression splines for inverse analysis of soil and wall properties in braced excavation , 2017 .

[98]  Chan-Young Yune,et al.  ANALYSIS OF DEBRIS FLOW BEHAVIOR USING AIRBORNE LIDAR AND IMAGE DATA , 2016 .

[99]  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..

[100]  Biswajeet Pradhan,et al.  Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations , 2016 .

[101]  Seyed Amir Naghibi,et al.  Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms , 2018, Bulletin of Engineering Geology and the Environment.

[102]  Richard M. Iverson,et al.  Debris-flow mobilization from landslides , 1997 .

[103]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[104]  Christian Scheidl,et al.  The use of airborne LiDAR data for the analysis of debris flow events in Switzerland , 2008 .

[105]  Hamid Reza Pourghasemi,et al.  Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping , 2018, Geoderma.

[106]  Seyed Amir Naghibi,et al.  A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.

[107]  Shelley A. Hinsley,et al.  Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships , 2015, Remote. Sens..

[108]  Gordon E. Grant,et al.  Debris flow initiation in proglacial gullies on Mount Rainier, Washington , 2014 .

[109]  Eric Bardou,et al.  Debris flow susceptibility mapping at a regional scale , 2008 .

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

[111]  Colm Jordan,et al.  The comparison between two airborne LiDAR datasets to analyse debris flow initiation in north-western Iceland , 2015 .

[112]  A. Zhu,et al.  A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) , 2017, Environmental Earth Sciences.

[113]  Amir Hossein Alavi,et al.  Progress of machine learning in geosciences：Preface , 2016 .

[114]  P. Reichenbach,et al.  Gis Technology in Mapping Landslide Hazard , 1995 .

[115]  F. Federico,et al.  An energy-based approach to predict debris flow mobility and analyze empirical relationships , 2015 .

[116]  Biswajeet Pradhan,et al.  Debris Flow Source Identification in Tropical Dense Forest Using Airborne Laser Scanning Data and Flow-R Model , 2017 .

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

[118]  P. Tarolli,et al.  The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology , 2008 .

[119]  Alessandro Simoni,et al.  Field observations of a debris flow event in the Dolomites , 1999 .

[120]  Aykut Akgün,et al.  Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey , 2011, Comput. Geosci..

[121]  Marwan A. Hassan,et al.  Debris flow initiation and sediment recharge in gullies , 2009 .

[122]  R J Fannin,et al.  An empirical-statistical model for debris flow travel distance , 2001 .

[123]  C. H. Abdullah,et al.  Rainfall Intensity and Duration for Debris Flow Triggering in Peninsular Malaysia , 2014 .

[124]  J. Greenberg,et al.  Assessing levee stability with geometric parameters derived from airborne LiDAR , 2012 .

[125]  Gordon E. Grant,et al.  Periglacial debris-flow initiation and susceptibility and glacier recession from imagery, airborne LiDAR, and ground-based mapping , 2012 .

[126]  J. Blaszczyński,et al.  LANDFORM CHARACTERIZATION WITH GEOGRAPHIC INFORMATION SYSTEMS , 1997 .

[127]  Nicolas Eckert,et al.  Rainfall control of debris-flow triggering in the Réal Torrent, Southern French Prealps , 2017 .

[128]  Sanjiban Sekhar Roy,et al.  Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM , 2018 .

[129]  Biswajeet Pradhan,et al.  Identification of Debris Flow Initiation Zones Using Topographic Model and Airborne Laser Scanning Data , 2017 .

[130]  M. Jaboyedoff,et al.  Debris flow modeling for susceptibility mapping at regional to national scale in Norway , 2012 .

[131]  Bin Li,et al.  Long-runout mechanism and landsliding behaviour of large catastrophic landslide triggered by heavy rainfall in Guanling, Guizhou, China , 2015 .

[132]  Markus Meinhardt,et al.  Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics , 2015 .

[133]  C. Gregoretti,et al.  GIS-based cell model for simulating debris flow runout on a fan , 2016 .

[134]  Paolo Tarolli,et al.  LiDAR-derived slopes for headwater channel network analysis , 2009 .

[135]  V. Singh,et al.  Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model , 2017 .

[136]  H. Pourghasemi,et al.  Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.

[137]  F. Catani,et al.  On the application of SAR interferometry to geomorphological studies: estimation of landform attributes and mass movements , 2005 .

[138]  E. Rotigliano,et al.  Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations , 2015, Natural Hazards.

[139]  Dieter Rickenmann,et al.  Debris-flow hazard assessment and methods applied in engineering practice , 2016 .

[140]  Weitao Chen,et al.  Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China , 2014 .

[141]  Haliza Abdul Rahman,et al.  Landslides disaster in Malaysia: an overview , 2017 .

[142]  E. Rotigliano,et al.  Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy) , 2015 .

[143]  Guangqi Chen,et al.  GIS-based numerical modelling of debris flow motion across three-dimensional terrain , 2013, Journal of Mountain Science.

[144]  Takahisa Mizuyama,et al.  Case study of debris flow disaster scenario caused by torrential rain on Kiyomizu-dera, Kyoto, Japan - using Hyper KANAKO system , 2016, Journal of Mountain Science.

[145]  E. J. Bee,et al.  Creating a Debris Flow Susceptibility Modelfor Great Britain: a GIS Based Approach , 2017 .

[146]  N. Eyles,et al.  LiDAR-based volume assessment of the origin of the Wadena drumlin field, Minnesota, USA , 2016 .

[147]  Ronald Chase,et al.  A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[149]  Mario Floris,et al.  Recent changes in rainfall characteristics and their influence on thresholds for debris flow triggering in the Dolomitic area of Cortina d'Ampezzo, north-eastern Italian Alps , 2010 .

[150]  Guifang Zhang,et al.  Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China , 2016 .

[151]  Jason W. Kean,et al.  Runoff‐generated debris flows: Observations and modeling of surge initiation, magnitude, and frequency , 2013 .

[152]  K. C. Slatton,et al.  Airborne Laser Swath Mapping: Achieving the resolution and accuracy required for geosurficial research , 2007 .

[153]  Bahareh Kalantar,et al.  Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) , 2018 .

[154]  Woody Sherman,et al.  Improved Docking of Polypeptides with Glide , 2013, J. Chem. Inf. Model..

[155]  Jeroen M. Schoorl,et al.  Calibration and resolution effects on model performance for predicting shallow landslide locations in Taiwan , 2011 .

[156]  Guodong Guo,et al.  Support Vector Machines Applications , 2014 .

[157]  P. J. García Nieto,et al.  Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain) , 2011, Math. Comput. Model..

[158]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines and neural network models for prediction of pile drivability , 2016 .

[159]  S. Schnabel,et al.  Modeling soil properties at a regional scale using GIS and Multivariate Adaptive Regression Splines , 2011 .

[160]  Martino Bernard,et al.  The Debris Flow Occurred at Ru Secco Creek, Venetian Dolomites, on 4 August 2015: Analysis of the Phenomenon, Its Characteristics and Reproduction by Models , 2018, Front. Earth Sci..

[161]  Zhang Yu,et al.  Model and method of debris flow risk zoning based on momentum analysis , 2008, Wuhan University Journal of Natural Sciences.

[162]  M. Y. Shi,et al.  Assessing Debris Flow Susceptibility in Mountainous Area of Beijing, China Using a Combination Weighting and an Improved Fuzzy C-means Algorithm , 2015 .

[163]  Clive Oppenheimer,et al.  GIS‐assisted modelling for debris flow hazard assessment based on the events of May 1998 in the area of Sarno, Southern Italy: II. Velocity and dynamic pressure , 2008 .

[164]  Patrick Kailey.  Debris flows in New Zealand Alpine Catchments , 2013 .

[165]  Ajoy Kumar Das,et al.  Application of Multivariate Adaptive Regression Spline-Assisted Objective Function on Optimization of Heat Transfer Rate Around a Cylinder , 2016 .

[166]  Zohre Sadat Pourtaghi,et al.  GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran , 2016, Environmental Earth Sciences.

[167]  D. Bui,et al.  Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.

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

[169]  Michel Jaboyedoff,et al.  Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale , 2013 .

[170]  Marcel Hürlimann,et al.  Field and laboratory analysis of the runout characteristics of hillslope debris flows in Switzerland , 2015 .

[171]  Bhavani Narayanaswamy,et al.  Improving marine habitat mapping using high-resolution acoustic data; a predictive habitat map for the Firth of Lorn, Scotland , 2018, Continental Shelf Research.

[172]  N. Pfeifer,et al.  Laser scanning – principles and applications , 2007 .

[173]  C. Westen,et al.  Evaluating the use of training areas in bivariate statistical landslide hazard analysis : a case study in Colombia , 1994 .

[174]  Alessandro Simoni,et al.  Experimental evidences and numerical modelling of debris flow initiated by channel runoff , 2005 .

[175]  D. Staley,et al.  Surficial patterns of debris flow deposition on alluvial fans in Death Valley, CA using airborne laser swath mapping data , 2006 .

[176]  C. Chen,et al.  Landslide and debris flow initiated characteristics after typhoon Morakot in Taiwan , 2016, Landslides.

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

[178]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[179]  Satari Siti Zanariah,et al.  Some Statistical Characteristic of Malaysian Wind Direction Recorded at Maximum Wind Speed: 1999-2008 , 2015 .

[180]  P. Behnia,et al.  Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada , 2015 .

[181]  J. Shan,et al.  Topographic laser ranging and scanning : principles and processing , 2008 .

[182]  Jiayong Deng,et al.  Analysis of the triggering conditions and erosion of a runoff-triggered debris flow in Miyun County, Beijing, China , 2018, Landslides.

[183]  Matjaž Mikoš,et al.  Topographic Data and Numerical Debris-Flow Modeling , 2013 .

[184]  Martin C. Towner,et al.  A new approach to estimating hazard posed by debris flows in the Westfjords of Iceland , 2010 .

[185]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[186]  Stefano Lanzoni,et al.  Relevance of erosion processes when modelling in-channel gravel debris flows for efficient hazard assessment , 2019, Journal of Hydrology.

[187]  S. Schnabel,et al.  Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies , 2009 .

[188]  Lorenzo Marchi,et al.  Monitoring sediment source areas in a debris-flow catchment using terrestrial laser scanning , 2014 .

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

[190]  Lorenzo Marchi,et al.  Characterisation of the surface morphology of an alpine alluvial fan using airborne LiDAR , 2008 .

[191]  N. K. Goel,et al.  Improving real time flood forecasting using fuzzy inference system , 2014 .

[192]  Tomislav Hengl,et al.  A Practical Guide to Geostatistical Mapping , 2009 .

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

[194]  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.

[195]  Byeong Seok Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[196]  B. Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[197]  Seung-Rae Lee,et al.  Coupled model for simulation of landslides and debris flows at local scale , 2016, Natural Hazards.

[198]  Soyoung Park,et al.  Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS , 2017 .

[199]  Wathiq Laftah Al-Yaseen,et al.  Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system , 2017, Expert Syst. Appl..

[200]  Qing Wang,et al.  Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods , 2013, Natural Hazards.

[201]  Sandra Melzner,et al.  Regional analysis of slope instability processes along the southern border of the central Tauern Window (Eastern Alps) , 2015 .

[202]  Liangjie Wang,et al.  Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models , 2015 .

[203]  Qibing Jin,et al.  A Novel Bacterial Algorithm for Parameter Optimization of Support Vector Machine , 2018, 2018 37th Chinese Control Conference (CCC).