A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

By Biswajeet Pradhan. Computational Geosciences, 2013

1,135 citations recorded by the source corpus.

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608pixels while total 8403pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8e98629f65d9517afa8546c12f787921cfc64cf0

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Citations in the corpus

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China (2019, background): “Plan curvature directly affects the convergence and dispersion of surface fluids (Nasiri Aghdam et al. 2016; Pourghasemi and Rossi 2016; Pradhan 2013; Pourghasemi et al. 2013).”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, background): “2012c), and adaptive neurofuzzy inference system (ANFIS) (Vahidnia et al. 2010; Oh and Pradhan 2011; Sezer et al. 2011; Tien Bui et al. 2011b; Pradhan 2013) have been proposed.”
Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, background): “In landslide modeling, Pradhan (2013) and Pham et al. (2015b) stated that the predictive capability of landslide models depends significantly on the quality of data.”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “In recent years, SVMs have been successfully applied to many problems including landslide susceptibility mapping (Pourghasemi et al. 2013; Pradhan 2013; Kavzoglu et al. 2014; Peng et al. 2014).”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, methodology): “99 by researchers around widely factors are objectively and preferentially selected show applied in the modeling and evaluation.”
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility (2017, background): “…Pradhan, 2010; Sharma et al., 2013), fuzzy rule-based classifier (Pham et al., 2016; Tien Bui et al., 2014), neuro fuzzy (Dehnavi et al., 2015; Pradhan, 2013), multivariate adaptive regression splines (MARS) (Conoscenti et al., 2015; Felicísimo et al., 2013; Vorpahl et al., 2012; 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, methodology): “, 2008; Yilmaz, 2010), decision tree method (Pradhan, 2013; Saito et al., 2009; Yeon et al., 2010), bivariate statistical (BS) analysis (Nandi and Shakoor, 2010; Xu et al.”
Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran (2015, methodology): “…been used for LSM such as neuro-fuzzy (Kanungo et al. 2006; Oh and Pradhan 2011; Pradhan 2013; Tien Bui et al. 2012), decision-tree (DT) methods (Pradhan 2013; Saito et al. 2009; Yeon et al. 2010), and support vector machine (SVM) (Brenning 2005; Peng et al. 2014; Pradhan 2013; Xu et al. 2012;…”
Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China (2016, methodology): “The ROC curve plots the false positive rate on the X-axis and true positive rate on the Y-axis, which shows the trade-off between the two rates (Pradhan 2013).”
Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide (2019, methodology): “The artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM) are three typical approaches for predicting the landslide displacement (Gao and Jiang 2012; Du et al. 2013; Pradhan 2013; Cao et al. 2016; Miao et al. 2017).”
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, methodology): “…Xu et al. 2012b; Devkota et al. 2013; Ozdemir and Altural 2013; Kundu et al. 2013; Park et al. 2013; Grozavu et al. 2013) and bivariate models (Pradhan and Youssef 2010; Pareek et al. 2010; Pradhan and Lee 2010; Pourghasemi et al. 2013a), has also been applied to landslide susceptibility mapping.”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “Machine learning approaches are considered more efficient than other approaches such as expert's opinion based methods and analytic methods for spatial eering, Gujarat Technological gar Highway, Chandkheda, pt@utt.edu.vn (B.T. Pham). prediction of landslides (Pradhan, 2013).”
Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China (2019, neutral): “2010; Pradhan 2012; Bui et al.”