GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory (70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party (ILWP), Forestry, Rangeland and Watershed Organisation (FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve (AUC), frequency ratio (FR) and seed cell area index (SCAI). Normalised difference vegetation index, land use/ land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models (AUC = 0.912 (91.2%) and 0.907 (90.7%), respectively) had high predictive accuracy than the LNRF model alone (AUC = 0.855 (85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

[1]  B. Pradhan,et al.  Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm , 2018, Land Degradation & Development.

[2]  Geping Luo,et al.  Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda , 2018, International journal of environmental research and public health.

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

[4]  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 .

[5]  J. Iqbal,et al.  Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China , 2017, Journal of Mountain Science.

[6]  K. Solaimani,et al.  Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran , 2012 .

[7]  B. Pradhan,et al.  Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. , 2019, Journal of environmental management.

[8]  Zahra Kalantari,et al.  Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure , 2018, Environmental Earth Sciences.

[9]  Davide Notti,et al.  Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees) , 2013 .

[10]  Bahareh Kalantar,et al.  Performance Evaluation and Sensitivity Analysis of Expert-Based, Statistical, Machine Learning, and Hybrid Models for Producing Landslide Susceptibility Maps , 2017 .

[11]  Frederic Masson,et al.  Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method , 2015 .

[12]  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.

[13]  C. Conoscenti,et al.  A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. , 2019, The Science of the total environment.

[14]  Hamid Reza Pourghasemi,et al.  Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling , 2017 .

[15]  PradhanBiswajeet.  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 .

[16]  A. Torkashvand,et al.  The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS) , 2014 .

[17]  H. Shahabi,et al.  Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. , 2018, Journal of environmental management.

[18]  J. Healey.  The Preparation: , 2019, Walter Baade.

[19]  J. N. Hutchinson,et al.  Suggested nomenclature for landslides , 1990 .

[20]  H. Pourghasemi,et al.  Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. , 2018, The Science of the total environment.

[21]  Iaeg Commission on Landslides.  Suggested nomenclature for landslides , 1990 .

[22]  Ping Liu,et al.  Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China , 2016, Environmental Earth Sciences.

[23]  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..

[24]  Wei Chen,et al.  Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques , 2017 .

[25]  P. Peduzzi,et al.  Global landslide and avalanche hotspots , 2006 .

[26]  S. M. Fatemi Aghda,et al.  Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran , 2017, Geotechnical and Geological Engineering.

[27]  Rajat Gupta,et al.  Landslide hazard zoning using the GIS approach—A case study from the Ramganga catchment, Himalayas , 1990 .

[28]  Hamid Reza Pourghasemi,et al.  Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R , 2019, Spatial Modeling in GIS and R for Earth and Environmental Sciences.

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

[30]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[31]  Qian Sheng,et al.  Effects of a controlling geological discontinuity on the seismic stability of an underground cavern subjected to near-fault ground motions , 2018, Bulletin of Engineering Geology and the Environment.

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

[33]  Wei Chen,et al.  Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia , 2018, Remote. Sens..

[34]  Jos Van Orshoven,et al.  Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA) , 2011, Environ. Model. Softw..

[35]  T. Kavzoglu,et al.  Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression , 2016 .

[36]  B. Pradhan,et al.  Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models , 2019, Geosciences Journal.

[37]  J. Friedman.  Stochastic gradient boosting , 2002 .

[38]  A. Zhu,et al.  GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method , 2018 .

[39]  Graham J. Williams,et al.  Data Mining with Rattle and R , 2013 .

[40]  Tae-Seob Kang,et al.  An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method , 2017, ISPRS Int. J. Geo Inf..

[41]  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.

[42]  A. Zhu,et al.  Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. , 2018, The Science of the total environment.

[43]  Mustafa Neamah Jebur,et al.  Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia , 2015, Environmental Earth Sciences.

[44]  Biswajeet Pradhan,et al.  Integration of LiDAR and QuickBird Data for Automatic Landslide Detection Using Object-Based Analysis and Random Forests , 2017 .

[45]  Nikolay Robinzonov,et al.  Advances in boosting of temporal and spatial models , 2013 .

[46]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[47]  Biswajeet Pradhan,et al.  Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping , 2018, Sensors.

[48]  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.

[49]  Tri Dev Acharya,et al.  Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .

[50]  D. Bui,et al.  A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India , 2017, International Journal of Sediment Research.

[51]  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.

[52]  H. Pourghasemi,et al.  GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique , 2018, Environmental Earth Sciences.

[53]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[54]  Nguyen Quoc Thanh,et al.  Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization , 2017, Landslides.

[55]  B. Pradhan.  Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia , 2010 .

[56]  A. Pradhan,et al.  Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea , 2014, Natural Hazards.

[57]  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.

[58]  Ying Wang,et al.  Landslide susceptibility mapping on a global scale using the method of logistic regression , 2017 .

[59]  Tamer Topal,et al.  GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) , 2012, Environmental Earth Sciences.

[60]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[61]  Maria Ferentinou,et al.  Shallow landslide susceptibility assessment in a semiarid environment — A Quaternary catchment of KwaZulu-Natal, South Africa , 2016 .

[62]  L. Breiman.  Arcing Classifiers , 1998 .

[63]  K. Solaimani,et al.  Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio , 2017, Environmental Earth Sciences.

[64]  Mousa Abedini,et al.  Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran , 2018, Environmental Earth Sciences.

[65]  Hamid Reza Pourghasemi,et al.  Identification of erosion-prone areas using different multi-criteria decision-making techniques and GIS , 2018 .

[66]  Wei Chen,et al.  A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment , 2018 .

[67]  Biswajeet Pradhan,et al.  An Assessment of the Use of an Advanced Neural Network Model with Five Different Training Strategies for the Preparation of Landslide Susceptibility Maps , 2021, Journal of Data Science.

[68]  B. Pradhan,et al.  Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy , 2016 .

[69]  Hamid Reza Pourghasemi,et al.  Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms , 2018, Applied Sciences.

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

[71]  B. Pham,et al.  Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods , 2017, Theoretical and Applied Climatology.

[72]  Michele Calvello,et al.  Susceptibility zoning of shallow landslides in fine grained soils by statistical methods , 2016 .

[73]  Dieu Tien Bui,et al.  A comparative study between popular statistical and machine learning methods for simulating volume of landslides , 2017 .

[74]  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.

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

[76]  Wenping Li,et al.  Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China , 2014, Arabian Journal of Geosciences.

[77]  A-Xing Zhu,et al.  Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. , 2018, The Science of the total environment.

[78]  A. Kawasaki,et al.  Landslide susceptibility mapping of the Sera River Basin using logistic regression model , 2017, Natural Hazards.

[79]  B. Pham,et al.  A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. , 2018, The Science of the total environment.

[80]  A. Zhu,et al.  An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic , 2014 .

[81]  Himan Shahabi,et al.  Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA) , 2019 .

[82]  Michele Calvello,et al.  A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils , 2017 .

[83]  Quoc-Phi Nguyen,et al.  A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS , 2017, Landslides.

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

[85]  Graham J. Williams.  Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery , 2011 .

[86]  Nikolas Prechtel,et al.  An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image , 2014, Comput. Geosci..

[87]  Nhat-Duc Hoang,et al.  A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides , 2018, Remote. Sens..

[88]  Hamid Reza Pourghasemi,et al.  Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China , 2018, Comput. Geosci..

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

[90]  Xiaoqin Li,et al.  GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China , 2015, Environmental Earth Sciences.

[91]  J. García-Davalillo,et al.  DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study , 2014, Landslides.

[92]  Hamid Reza Pourghasemi,et al.  Applying different scenarios for landslide spatial modeling using computational intelligence methods , 2017, Environmental Earth Sciences.

[93]  Alireza Arabameri,et al.  GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. , 2019, The Science of the total environment.

[94]  D. Montgomery,et al.  Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China , 2015 .

[95]  Wei Chen,et al.  Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms , 2018, Sensors.

[96]  B. Ripley,et al.  Recursive Partitioning and Regression Trees , 2015 .

[97]  V. Singh,et al.  New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling , 2018, Water.

[98]  Lal Samarakoon,et al.  Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka) , 2018 .

[99]  Yoshikazu Yamaguchi,et al.  Application of exterior deformation monitoring of dams by DInSAR analysis using ALOS PALSAR , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[100]  M. Rossi,et al.  Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods , 2017, Theoretical and Applied Climatology.