Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features

Landslide disasters cause huge casualties and economic losses every year, how to accurately forecast the landslides has always been an important issue in geo-environment research. In this paper, a hybrid machine learning approach RSLMT is firstly proposed by coupling Random Subspace (RS) and Logistic Model Tree (LMT) for producing a landslide susceptibility map (LSM). With this method, the uncertainty introduced by input features is considered, the problem of overfitting is solved by reducing dimensions to increase the prediction rate of landslide occurrence. Moreover, the uncertainty of prediction will be deeply discussed with the rank probability score (RPS) series, which is an important evaluation of uncertainty but rarely used in LSM. Qingchuan county, China was taken as a study area. 12 landslide causal factors were selected and their contribution on landslide occurrence was evaluated by ReliefF method. In addition, Logistic Model Tree (LMT), Naive Bayes (NB) and Logistic Regression (LR) were researched for comparison. The results showed that RSLMT (AUC = 0.815) outperformed LMT (AUC = 0.805), NB (AUC = 0.771), LR (AUC = 0.785). LSM of Qingchuan county was produced using the novel model, it indicated that landslides tend to occur along with the fault belts and the middle-low mountain area that is strongly influenced by the large numbers of human engineering activities.

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

[2]  Dong-Kun Lee,et al.  Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods , 2018, Stochastic Environmental Research and Risk Assessment.

[3]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[4]  D. Bui,et al.  Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees , 2018 .

[5]  Edward S. Epstein,et al.  A Scoring System for Probability Forecasts of Ranked Categories , 1969 .

[6]  Paraskevas Tsangaratos,et al.  Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size , 2016 .

[7]  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 .

[8]  Andrzej Borkowski,et al.  Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland , 2017, Natural Hazards.

[9]  Wei Chen,et al.  A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China , 2017 .

[10]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[11]  D. Mercier,et al.  Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland , 2014 .

[12]  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.

[13]  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..

[14]  R. Anbalagan,et al.  Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand , 2016, Journal of the Geological Society of India.

[15]  J. Friedman,et al.  Classification and Regression Trees (Wadsworth Statistics/Probability) , 1984 .

[16]  Aytug Onan,et al.  Classifier and feature set ensembles for web page classification , 2016, J. Inf. Sci..

[17]  Alberto Refice,et al.  Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment , 2002 .

[18]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Seung-Rae Lee,et al.  A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea , 2016 .

[20]  Ying Wang,et al.  Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm , 2016 .

[21]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[22]  Wei Chen,et al.  Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.

[23]  Biswajeet Pradhan,et al.  Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area , 2011, Comput. Geosci..

[24]  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.

[25]  Robert P. W. Duin,et al.  Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.

[26]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[27]  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.

[28]  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.

[29]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[30]  Christos Polykretis,et al.  Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale , 2016 .

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

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

[33]  R. Dahal,et al.  Rainfall event-based landslide susceptibility zonation mapping , 2013, Natural Hazards.

[34]  Guang-qi Chen,et al.  Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network , 2012 .

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

[36]  S. Río,et al.  Links between teleconnection patterns and mean temperature in Spain , 2015, Theoretical and Applied Climatology.

[37]  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..

[38]  Himan Shahabi,et al.  A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment , 2018, Geocarto International.

[39]  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 .

[40]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[41]  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 .

[42]  HoTin Kam.  The Random Subspace Method for Constructing Decision Forests , 1998 .

[43]  S. Kalogirou,et al.  Landslide susceptibility, Peloponnese Peninsula in South Greece , 2014 .

[44]  Biswajeet Pradhan,et al.  Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS , 2012, Comput. Geosci..

[45]  Marian Dardala,et al.  Using CUDA to accelerate uncertainty propagation modelling for landslide susceptibility assessment , 2019, Environ. Model. Softw..

[46]  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 .

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

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

[49]  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.