Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

Abstract

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.

Geoinformation-based landslide susceptibility mapping in subtropical area

“…As we have noted, road construction is the most important geo-environmental factor provoking landslides and this confirms what we have observed in previous studies 26 – 28 , 36 . This requires our attention to the potential disaster that may be induced while planning future urbanization and road development.…”

“…A value of 1 was assigned to landslides and 0 to non-landslide points. As proposed by Zhang et al 27 , Huangfu et al 36 , Ou et al 26 , and Zhou et al 28 , 70% of the landslides and non-landslide samples were randomly picked out to constitute a training set (TS) to model landslide susceptibility, and the remained landslides and non-landslide samples (30%) as a validation set (VS) to evaluate the accuracy of modeling.

Figure 3 Photos of the rainfall triggered landslides in the study area.

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“…The occurrence of landslides is a consequence of the long-term joint action of the endogenous factors, i.e., geology, landform, vegetation and soil, etc., and the short-term predisposing factors, i.e., rainfall, earthquake and anthopogenic activities 18 , 27 . According to previous research on the landslide-causative factors 27 , 28 , 36 and landslide field investigation in Chongren, geological and geomorphological data, hydrological data, land cover and transport system data were used to establish geoinformation datasets for landslide hazard analysis.…”

“…Meanwhile, it is highly efficient in calculating high-dimension data and can fit the nonlinear relationships between target and factors 8 , 29 – 31 . Nevertheless, the prediction accuracy of the most studies, even including those harnessing the hotspotted deep learning techniques 32 – 35 , comes between 75 and 85%, except for those of Huangfu et al 36 , Ou et al 26 , Zhang et al 27 and Zhou et al 28 , who have achieved landslide risk prediction with an accuracy of 86–94.54%. This is not ideal for government to target effectively and accurately the high risk zones for implementing disaster reduction and prevention measures in the subtropical areas.…”

“…Hybrid models are in fact constructed by integrating two or more models in aspect of sample selection 28 , 37 , feature selection 21 , 38 , information extraction and finally landslide hazard prediction with reasonable accuracy 10 , 22 , 25 , 39 – 41 . Hence, hybrid modeling has gained recently a momentum in improving the accuracy and reliability of landslide risk mapping 26 , 36 , 40 , 42 , 43 . However, there are still uncertainty in processing both categorical and continuous factors which may influence directly the prediction accuracy.…”