A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam

In this study, we proposed a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility mapping at the Kon Tum Province, Viet Nam. Landslide affecting factors (slope angle, slope aspect, elevation, valley depth, land use, NDVI, soil type, lithology, distance to geology boundaries, and distance to faults), and 1404 past and current landslide locations have been first collected from the study area for generating training and testing datasets. Secondly, the hybrid model RFFT has been constructed for landslide susceptibility assessment using training dataset. Performance of the proposed RFFT model has been validated by analysis of the Receiver Operating Characteristic (ROC) curve and statistical indexes, and compared with a well-known landslide models namely Support Vector Machines (SVM) and the single FT. Results show that the proposed RFFT model has good performance for landslide susceptibility assessment. It has better predictive capability compared with well-known SVM model and single FT model. Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

[1]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[2]  João Gama,et al.  Functional trees for classification , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[3]  I. Jolliffe.  Principal Component Analysis , 2002 .

[4]  John C. Davis,et al.  Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .

[5]  C. Gokceoglu,et al.  Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques , 2004 .

[6]  João Gama,et al.  Functional Trees , 2001, Machine Learning.

[7]  L. Ayalew,et al.  Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .

[8]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[9]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Juan José Rodríguez Diez,et al.  An Experimental Study on Rotation Forest Ensembles , 2007, MCS.

[11]  De-Shuang Huang,et al.  Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.

[12]  H. A. Nefeslioglu,et al.  Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey , 2010 .

[13]  Arif Gülten,et al.  A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases , 2012, Journal of Medical Systems.

[14]  Arif Gülten,et al.  Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..

[15]  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..

[16]  Akin Ozcift,et al.  SVM Feature Selection Based Rotation Forest Ensemble Classifiers to Improve Computer-Aided Diagnosis of Parkinson Disease , 2012, Journal of medical systems.

[17]  Sitaram Nayak,et al.  A Critical Review of Construction, Analysis and Behaviour of Stone Columns , 2013, Geotechnical and Geological Engineering.

[18]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[19]  Taskin Kavzoglu,et al.  An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping , 2013 .

[20]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[21]  T. Kavzoglu,et al.  Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression , 2014, Landslides.

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

[23]  Peijun Du,et al.  Hyperspectral Remote Sensing Image Classification Based on Rotation Forest , 2014, IEEE Geoscience and Remote Sensing Letters.

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

[25]  Fengyin Liu,et al.  Study on the Reasonable Height of Embankment in Qinghai–Tibet Highway , 2016, Geotechnical and Geological Engineering.

[26]  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.

[27]  Heping Tao,et al.  Dynamic monitoring of soil wind erosion in Inner Mongolia of China during 1985–2011 based on geographic information system and remote sensing , 2016, Natural Hazards.

[28]  Inge Revhaug,et al.  Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan , 2015, PloS one.

[29]  Study of the Earth’s crustal structure in the Area of Song Tranh and it’s adjacents , 2015 .

[30]  Iman Nasiri Aghdam,et al.  A new hybrid model using Step-wise Weight Assessment Ratio Analysis (SWARA) technique and Adaptive Neuro-fuzzy Inference System (ANFIS) for regional landslide hazard assessment in Iran , 2015 .

[31]  Values for peak ground acceleration and peak ground velocity using in seismic hazard assessment for Song Tranh 2 hydropower region , 2015 .

[32]  Binh Thai Pham,et al.  Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS , 2016 .

[33]  Guo Huadong,et al.  Digital Earth and Future Earth , 2016 .

[34]  Indra Prakash,et al.  Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen Bai Province (Viet Nam) , 2017, Journal of the Indian Society of Remote Sensing.

[35]  D. Chin.  Designing Bioretention Areas for Stormwater Management , 2017, Environmental Processes.

[36]  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..

[37]  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.

[38]  Monitoring of Aphis gossypii Using Greenseeker and SPAD Meter , 2017, Journal of the Indian Society of Remote Sensing.

[39]  B. Pham,et al.  A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area , 2016, Geotechnical and Geological Engineering.

[40]  N. Phuong,et al.  PROBABILISTIC SEISMIC HAZARD ASSESSMENT FOR THE TRANH RIVER HYDROPOWER PLANT NO2 SITE, QUANG NAM PROVINCE , 2016 .

[41]  Simulated ground motion of the earthquake on Octorber 22nd, 2012, M4.6 at Song Tranh 2 dam area , 2016 .

[42]  Nhat-Duc Hoang,et al.  Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam , 2016, Int. J. Digit. Earth.

[43]  B. Faybishenko,et al.  Permeability variations within mining-induced fractured rock mass and its influence on groundwater inrush , 2016, Environmental Earth Sciences.

[44]  B. Pradhan,et al.  GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.

[45]  About factors related to the occurrence of earthquakes in the Song Tranh 2 hydropower area in period 2011-2014 , 2016 .

[46]  B. Pham,et al.  Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS , 2016, Natural Hazards.

[47]  D. Bui,et al.  A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS , 2017 .

[48]  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.

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

[50]  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 .

[51]  Dieu Tien Bui,et al.  Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study , 2017, Geotechnical and Geological Engineering.