Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece

被引:143
|
作者
Tsangaratos, Paraskevas [1 ]
Ilia, Ioanna [1 ]
机构
[1] Natl Tech Univ Athens, Sch Min & Met Engn, Dept Geol Studies, Zografou Campus Heroon Polytech 9, Zografos 15780, Greece
关键词
Landslide susceptibility; Decision tree; Certainty factor; Greece; ARTIFICIAL NEURAL-NETWORKS; ANALYTICAL HIERARCHY PROCESS; LOGISTIC-REGRESSION; CERTAINTY FACTOR; FREQUENCY RATIO; SPATIAL PREDICTION; DEMPSTER-SHAFER; CONDITIONAL-PROBABILITY; SLOPE INSTABILITY; FUZZY MODELS;
D O I
10.1007/s10346-015-0565-6
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models.
引用
收藏
页码:305 / 320
页数:16
相关论文
共 50 条
  • [1] Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece
    Paraskevas Tsangaratos
    Ioanna Ilia
    [J]. Landslides, 2016, 13 : 305 - 320
  • [2] Landslide susceptibility mapping in Injae, Korea, using a decision tree
    Yeon, Young-Kwang
    Han, Jong-Gyu
    Ryu, Keun Ho
    [J]. ENGINEERING GEOLOGY, 2010, 116 (3-4) : 274 - 283
  • [3] Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping
    Zhang, Qi
    Ning, Zixin
    Ding, Xiaohu
    Wu, Junfeng
    Wang, Zhao
    Tsangaratos, Paraskevas
    Ilia, Ioanna
    Wang, Yukun
    Chen, Wei
    [J]. WATER, 2024, 16 (05)
  • [4] Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
    Wu, Yanli
    Ke, Yutian
    Chen, Zhuo
    Liang, Shouyun
    Zhao, Hongliang
    Hong, Haoyuan
    [J]. CATENA, 2020, 187
  • [5] Decision tree based ensemble machine learning approaches for landslide susceptibility mapping
    Arabameri, Alireza
    Chandra Pal, Subodh
    Rezaie, Fatemeh
    Chakrabortty, Rabin
    Saha, Asish
    Blaschke, Thomas
    Di Napoli, Mariano
    Ghorbanzadeh, Omid
    Thi Ngo, Phuong Thao
    [J]. GEOCARTO INTERNATIONAL, 2022, 37 (16) : 4594 - 4627
  • [6] Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea
    Park, Sung-Jae
    Lee, Chang-Wook
    Lee, Saro
    Lee, Moung-Jin
    [J]. REMOTE SENSING, 2018, 10 (10)
  • [7] Modeling landslide susceptibility using alternating decision tree and support vector
    Chen, Zhuo
    Tang, Junfeng
    Song, Danqing
    [J]. TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES, 2024, 35 (01):
  • [8] Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling
    He, Qingfeng
    Xu, Zhihao
    Li, Shaojun
    Li, Renwei
    Zhang, Shuai
    Wang, Nianqin
    Binh Thai Pham
    Chen, Wei
    [J]. ENTROPY, 2019, 21 (02)
  • [9] Spatial assessment of landslide susceptibility mapping generated by fuzzy-AHP and decision tree approaches
    Saygin, Fikret
    Sisman, Yasemin
    Dengiz, Orhan
    Sisman, Aziz
    [J]. ADVANCES IN SPACE RESEARCH, 2023, 71 (12) : 5218 - 5235
  • [10] The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China
    Zhang, Kaixiang
    Wu, Xueling
    Niu, Ruiqing
    Yang, Ke
    Zhao, Lingran
    [J]. ENVIRONMENTAL EARTH SCIENCES, 2017, 76 (11)