GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling

• Published in: Catena (Giessen) 2021-08, Vol.203, p.105344, Article 105344
• Main Authors: Chen, Wenwu, Zhang, Shuai
• Format: Article
• Language: English
• Subjects: Frequency Ratio; Landslide susceptibility assessment; Machine learning; Muchuan County
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2021.105344

[Display omitted] •Hoeffding Tree is a promising landslide susceptibility model based on ROC and AUC.•Bayes Network and Logistic Model Tree were applied for comparison.•Correlation analysis of conditioning factors was completed by Frequency Ratio.•Landslide susceptibility maps of Muchuan County and the county town were produced. Landslides, one of the most common hazards around the world, have brought about severe damage to life and property of human. To prevent and mitigate landslides, various models have been introduced to assess landslide susceptibility. In this paper, Hoeffding Tree (HT), a prevailing data stream mining algorithm, was employed to predict landslide susceptibility in Muchuan County, China for the first time. Meanwhile, Logistic Model Tree (LMT) and Bayes Network (BN) were applied to produce landslide susceptibility maps for comparison. The model performances were evaluated by Receiver Operating Characteristic (ROC) curves and areas under the curves (AUC). To obtain landslide inventory map, 279 landslides data was collected, and training and validation datasets were randomly divided with a proportion of 70% to 30%. Furthermore, twelve conditioning factors (altitude, slope angle, profile curvature, plan curvature, slope aspect, distance to roads, distance to rivers, TWI, NDVI, soil, land use and lithology) were selected to construct landslide susceptibility models. Moreover, correlations between conditioning factors and landslides were analyzed using Frequency Ratio (FR). The results showed landslides are prone to occur in areas where human activities concentrate, and all three models exhibited satisfying performances. Concretely, for training dataset, LMT model showed the highest AUC (0.854), followed by HT (0.726) and BN (0.709). However, for validation dataset, LMT and BN models generated similar AUC values (0.761 and 0.764 respectively), and the highest AUC value belonged to HT (0.802). The distributions of landslide susceptibility zones revealed that the interior of county town is mainly seated in low and very low susceptibility zones, whereas regions close to the border suffer high and very high landslide risk. The results acquired in this paper are significant to landslide prevention and urban planning in Muchuan, China. Additionally, this study proved that HT model is a promising classifier for landslide susceptibility modeling.