Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naive Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos and geological field surveys, the 263 landslide locations in the study area were obtained. Next, the identified landslides were randomly classified according to the ratio of 70/30 to construct training data and validation models, respectively. Secondly, based on the landslide inventory map, combined with the geological and geomorphological characteristics of the study area, 14 affecting factors of the landslide were determined. The predictive ability of the selected factors was evaluated using the LSVM model. Using the WoE model, the relationship between landslides and affecting factors was analyzed by positive and negative correlation methods. The above three hybrid models were then used to map landslide susceptibility. Thirdly, the ROC curve and various statistical data (SE, 95% CI and MAE) were used to verify and compare the predictive power of the model. Compared with the other two models, the Sysfor model had a larger area under the curve (AUC) of 0.876 (training dataset) and 0.783 (validation dataset). Finally, by quantitatively comparing the susceptibility values of each pixel, the differences in spatial morphology of landslide susceptibility maps were compared, and the model was found to have limitations and effectiveness. The landslide susceptibility maps obtained by the three models are reasonable, and the landslide susceptibility maps generated by the SysFor model have the highest comprehensive performance. The results obtained in this paper can help local governments in land use planning, disaster reduction and environmental protection.

[1]  S. Anbazhagan,et al.  Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models , 2015, Environmental Earth Sciences.

[2]  S. Mandal,et al.  Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India , 2020, Geocarto International.

[3]  F. Bulut,et al.  Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey) , 2007 .

[4]  Biswajeet Pradhan,et al.  Application of an evidential belief function model in landslide susceptibility mapping , 2012, Comput. Geosci..

[5]  Wen-Chieh Chou,et al.  Assessment of vegetation recovery and soil erosion at landslides caused by a catastrophic earthquake: A case study in Central Taiwan , 2006 .

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

[7]  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..

[8]  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.

[9]  A. Zhu,et al.  Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping , 2019, CATENA.

[10]  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.

[11]  Wei Chen,et al.  Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques , 2019, Water.

[12]  Shiuan Wan,et al.  A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map , 2009 .

[13]  T. Topal,et al.  GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey) , 2003 .

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

[15]  Philippa J. Mason,et al.  Landslide hazard assessment in the Three Gorges area of the Yangtze river using ASTER imagery: Zigui–Badong , 2004 .

[16]  H. Pourghasemi,et al.  Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances , 2013, Natural Hazards.

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

[18]  T. Kavzoglu,et al.  Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm , 2015 .

[19]  Wei Chen,et al.  Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping , 2020, Symmetry.

[20]  Sharon Nielsen,et al.  Self-disclosure on Facebook among female users and its relationship to feelings of loneliness , 2014, Comput. Hum. Behav..

[21]  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 .

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

[23]  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.

[24]  B. Pradhan,et al.  Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models , 2012 .

[25]  Franck Lavigne,et al.  Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia , 2017, Natural Hazards.

[26]  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 .

[27]  Xiaojing Wang,et al.  Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression , 2018, Applied Sciences.

[28]  Dieu Tien Bui,et al.  A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling , 2019, Bulletin of Engineering Geology and the Environment.

[29]  Wei Chen,et al.  GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques , 2019, Applied Sciences.

[30]  Wei Chen,et al.  Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models , 2019, Geoderma.

[31]  Wei Chen,et al.  Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree , 2019, Geocarto International.

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

[33]  N. Bhandary,et al.  Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia , 2016, Geoenvironmental Disasters.

[34]  L. Prandini,et al.  Behavior of the vegetation in slope stability: A critical review , 1977 .

[35]  Rubini Mahalingam,et al.  Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study) , 2016 .

[36]  Biswajeet Pradhan,et al.  An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data , 2018, CATENA.

[37]  Yong Qin,et al.  Ionic composition, geological signature and environmental impacts of coalbed methane produced water in China , 2021, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[38]  Khabat Khosravi,et al.  Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India , 2017, Environmental Processes.

[39]  D. Bui,et al.  Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches , 2019, CATENA.

[40]  Aykut Akgün,et al.  GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region , 2007 .

[41]  Bodo Damm,et al.  Spatial databases and GIS as tools for regional landslide susceptibility modeling , 2014 .

[42]  Cheng Li,et al.  Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China , 2019, Applied Sciences.

[43]  Wei Chen,et al.  GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models , 2020, CATENA.

[44]  Gökhan Demir,et al.  Landslide susceptibility mapping by using statistical analysis in the North Anatolian Fault Zone (NAFZ) on the northern part of Suşehri Town, Turkey , 2018, Natural Hazards.

[45]  S. Mickovski,et al.  Hydrological effect of vegetation against rainfall-induced landslides , 2017 .

[46]  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 .

[47]  Wei Chen,et al.  Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation , 2020, Remote. Sens..

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

[49]  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.

[50]  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..

[51]  E. Rotigliano,et al.  Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy) , 2015, Natural Hazards.

[52]  S. Gruber,et al.  Permafrost in steep bedrock slopes and its temperature‐related destabilization following climate change , 2007 .

[53]  Samuele Segoni,et al.  A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches , 2019, Landslides.

[54]  K. Beven,et al.  Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments , 1984 .

[55]  Himan Shahabi,et al.  Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. , 2019, The Science of the total environment.

[56]  Saro Lee,et al.  Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree , 2018, Applied Sciences.

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

[58]  Yi Wang,et al.  Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , 2019, Applied Sciences.

[59]  P. Reichenbach,et al.  Comparing landslide inventory maps , 2008 .

[60]  Huiming Tang,et al.  Dynamics characteristic of red clay in a deep-seated landslide, Northwest China: An experiment study , 2018 .

[61]  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.

[62]  Biswajeet Pradhan,et al.  A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) , 2016, Environ. Model. Softw..

[63]  B. Pradhan.  Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches , 2010 .

[64]  Luigi Borrelli,et al.  Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map , 2018, Landslides.

[65]  Md Zahidul Islam,et al.  Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem , 2014, Science and Engineering Ethics.

[66]  Fan Yang,et al.  GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models , 2016, Environmental Earth Sciences.

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

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

[69]  M. Ada,et al.  Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey , 2017, Natural Hazards.

[70]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[71]  Netra R. Regmi,et al.  Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA , 2010 .

[72]  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.

[73]  Wei Chen,et al.  Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China) , 2019, Bulletin of Engineering Geology and the Environment.

[74]  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 .

[75]  Saro Lee,et al.  Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea , 2007 .

[76]  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 .

[77]  Xi Chen,et al.  Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions , 2020, ISPRS Int. J. Geo Inf..

[78]  Wei Chen,et al.  Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping , 2020 .

[79]  M. Arora,et al.  Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model , 2010 .

[80]  Friday Uchenna Ochege,et al.  Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. , 2019, The Science of the total environment.

[81]  Saro Lee,et al.  Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea , 2018 .

[82]  R. Soeters,et al.  Landslide hazard and risk zonation—why is it still so difficult? , 2006 .

[83]  Tao Guo,et al.  Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China) , 2018, ISPRS Int. J. Geo Inf..

[84]  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.

[85]  Binh Thai Pham,et al.  A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility , 2018, Journal of the Geological Society of India.