Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

Abstract

The occurrence of landslide in the hilly region of South Korea is a matter of serious concern. This study tries to produce landslide susceptibility maps for Jumunjin Country in South Korea. Three machine learning algorithms, namely Logistic Regression (LR), LogitBoost (LB), and NaïveBayes (NB) are used, and their final model outcomes are compared to each other. Firstly, a landslide inventory map and the associated input data layers of the landslide conditioning factors were developed based on field verification, historical records, and high-resolution remote-sensing data in the geographic information system (GIS) environment. Seventeen landslide conditioning factors were prepared, including aspect, slope, altitude, maximum curvature, profile curvature, topographic wetness index (TWI), topographic positioning index (TPI), distance from fault, convexity, forest type, forest diameter, forest density, land use/land cover, lithology, soil, flow accumulation, and mid slope position. The result showed that the area under the curve (AUC) values of LR, LB, and NB models were 84.2%, 70.7%, and 85.2%, respectively. The results revealed that the LR and LB models produced reasonable accuracy than respect to NB model in landslide susceptibility assessment. The final susceptibility maps would be useful for preliminary land-use planning and hazard mitigation purpose.

A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

“…ROC curves were calculated for all three resulting landslide susceptibility maps. The vertical axis indicates the TPR, while the horizontal axis shows the FPR [79] (see Figure 6). TPRs are the pixels that correctly referred to the landslide areas, while FPRs are the pixels wrongly labelled as landslides.…”

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

“…Pourghasemi et al [10], in their paper entitled "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms", perform landslide susceptibility mapping using three different models of LogitBoost (LB), logistic regression (LR) and naïve Bayes (NB). The study area is Jumunjin, South Korea.…”

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

“…Moreover, the minimum, maximum, and standard deviation of the area of the landslide polygons were 0.07, 16, and 2.17 ha, respectively. Along with PlanetScope images (Table 1), the normalized difference vegetation index (NDVI) index-which is widely applied in landslide modeling [1,7,44]-was calculated from the NIR and Red bands to be used in landslide detection. Th probability of landslide occurrence is highly dependent on the surface topography; in other words, hilly and mountainous areas have the highest probability of landslide occurrence [1].…”

“…As well as the physical impacts on the environment, landslides also have adverse consequences for the economy of local communities [4,5]. Landslides can occur for a range of reasons; for instance, they can be triggered by earthquake shocks, heavy rainfall, or road construction in hilly areas [6,7]. Despite some progress being obtained through scientific studies, landslide susceptibility modeling and mapping pose significant challenges for land-use planners and policymakers [8,9].…”