Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

This paper aims to propose an efficient landslide susceptibility prediction (LSP) model based on the frequency ratio method and C5.0 Decision Tree (C5.0 DT) model. Taking Ruijin City as the study area, local landslide inventory and 12 environmental factors are collected. Then the nonlinear correlations between landslide inventory and environmental factors are established by frequency ratio (FR) method. Thirdly, the FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models; landslide samples and non-landslide samples are set as the output variables with values of 1 and 0, respectively. The mathematical relationship between input variables and output variables is established by C5.0 DT/SVM models. Finally, the performance of LSP of both models is evaluated by the Area Under Receiver Operation Characteristic Curve (AUC). Results show that: 1) The landslide susceptibility mapping (LSM) of the C5.0 DT and the SVM models are similar on the whole, high and very high susceptibility levels are mainly distributed in the north and in the edge of the study area. 2) The AUC values of C5.0 DT and SVM are 0.886 and 0.819, respectively. Both models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM. 3) It is significant and reliable to carry out LSP based on frequency ratio method and C5.0 DT model.

1 Introduction

China’s geological environment is fragile and geological disasters occur frequently. Especially in the southwest and south area of China, landslides are densely distributed, causing serious casualties and heavy economic losses (Fu et al., 2017; Zheng et al., 2019b; Yang et al., 2020a). Therefore, landslide susceptibility prediction (LSP) is very important, which can provide a scientific and effective basis for the prevention and control of landslide geological hazards, road planning, and the formulation of appropriate risk mitigation strategies in the southwestern and southern areas of China (Akgun et al., 2012; Bai et al., 2015; Jiang et al., 2017; Lin et al., 2021; Huang et al., 2022a).

The study of LSP is to estimate the spatial probability of landslide occurrence in a certain area so as to effectively provide early warning to high landslide-prone area and reduce the occurrence of related safety accidents (Bui et al., 2020). LSP models are generally classified into deterministic, heuristic and machine learning models (Huang et al., 2017b; Huang et al., 2020b). At present, there are many LSP methods, including Remote Sensing (RS) research on surface deformation area to predict the occurrence of landslides (Chang et al., 2020; Dai et al., 2021; Zhu et al., 2022), and to establish a LSP model coupled with multiple connection methods. Among them, the connection method of landslide inventory and environmental factors, and the determination of LSP models play an important role in the LSP modelling (Huang et al., 2020a). The LSP modelling mainly includes three processes: one is landslide inventory and division of prediction units; the second is the core process including selecting appropriate basic environmental factors and establishing models, and the final step is the LSP results testing (Dou et al., 2019).

At present, the LSP models coupled with various connection methods mainly include analytic hierarchy process (AHP) model (Ma et al., 2021), information value (IV) model (Zhao B. et al., 2021), mathematical statistics (Kouhartsiouk and Perdikou 2021) and machine learning models, where the machine learning models applied to LSP usually refer to C5.0 Decision Tree (C5.0 DT) (Mao et al., 2017), logistic regression (Shahabi et al., 2015), artificial neural network (Huang et al., 2019), extreme learning machine (Huang et al., 2017a), support vector machine (SVM) (Marjanović et al., 2011), gray correlation degree (Yu et al., 2021), random forest (Sun et al., 2020), clustering algorithm (Guo Z. et al., 2021), semi-supervised multilayer perceptron model (Huang et al., 2020b), etc. In general, the machine learning models are considered to have higher LSP performance than those of the heuristic models and conventional statistical models, due to their efficient nonlinear prediction abilities of machine learning models (Xiao et al., 2021; Wang et al., 2022). The most suitable model for LSP is inconclusive. Among many models, C5.0 DT and SVM models have widely-recognized nonlinear prediction performance (Chang et al., 2020; Li W. et al., 2021; Su et al., 2021). Both models have achieved ideal LSP results.

For the above-mentioned machine learning such as C5.0 DT and SVM, it is necessary to connect the landslide susceptibility index and environmental factors. There is a complex nonlinear correlation between environmental factors (Zhu et al., 2022). According to many studies (Chang et al., 2020; Huang et al., 2021a; Li W. et al., 2021; Guo Z. et al., 2021), the accuracy of the LSP model without considering the nonlinear connection is slightly lower than that of the coupled model considering the connection. The connection methods include the weight of evidence (Wu R. et al., 2020), IV model (Zhao B. et al., 2021) and the frequency ratio (FR) method (Nanda et al., 2020). FR method is used to characterize the spatial relationship between landslide distribution and condition factors. Machine learning model based on FR method is commonly used in LSP(Li et al., 2020; Huang et al., 2021b; Zhu et al., 2022). Therefore, this study intends to use the FR method to establish the nonlinear relationship between landslides and environmental factors. For the LSP modelling by the FR-C5.0 DT/SVM models, firstly, the FR values of the environmental factors are set as the input variables of the C5.0 DT and SVM models, then the C5.0 DT and SVM models for LSP are further established through training and testing the input-output variables.

In this study, Ruijin City in China is taken as the research area. A total of 12 basic environmental factors are obtained by remote sensing and ArcGIS 10.3 software. The C5.0 DT/SVM models are then used for LSP in Ruijin City. Then, the area under receiver operating characteristic curve (AUC) is used to assess the accuracy of LSP results of the C5.0 DT/SVM models (Panchal and Shrivastava 2021).

2 Landslide Susceptibility Prediction Modelling

2.1 Research Framework

In this study, the FR method is used to connect the C5.0 DT/SVM models with the environmental factors of landslide inventory to perform the LSP and analyze their results. The main contents (Figure 1) include:

1) Based on the landslide inventory Information and geological environment, a total of 12 basic environmental factors are acquired (Jiang et al., 2018b; Xiao et al., 2021).

2) The nonlinear correlation between each environmental factors and the relative density of landslides is established by FR method, and the importance of various environmental factors are quantified (Huang et al., 2022b).

3) The FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models, the output variables are landslide and non-landslide (marked as 1 and 0, respectively). The mathematical relationship between input variables and output variables is established (Liu et al., 2021).

4) The input-output variables are randomly divided by 70 and 30% as training dataset and test dataset, respectively. Training dataset is used for model training and building, test dataset is used to evaluate the generalization capability of the final model (Sun et al., 2020).

5) The performance of LSP of the two models is evaluated based on various indicators such as ROC (Xiao et al., 2020).

FIGURE 1

FIGURE 1. The flow chart of research framework.

2.2 Frequency Ratio Method

The FR method can reflect the response relationship between landslides and basic environmental factors, and characterize the relative influence degree of each attribute interval of environmental factors on the occurrence of landslides (Zhang Y.-x. et al., 2020). If FR values are greater than 1, it indicates that the corresponding environmental factors are conducive to promoting the occurrence of landslides. If FR values are close to 1, it indicates that the relationship between the environmental factors and the occurrence of landslides in the corresponding interval is weak. If FR values are less than 1, it indicates that landslides are not likely to occur in this attribute interval. Based on the FR method and the natural break method, the environmental factors are divided into eight attribute interval levels, and the discrete lithology factors are classified according to the actual state as the interval classification basis. The formula for calculating the FR values of each environmental factor is:

$FR=\frac{A_i/A}{B_i/B}(1)$

where A_i is the number of landslide grids in the interval for each type of environmental factor, A is the total number of landslide grids in the area, B_i is the total number of grids for each type of environmental factor in the corresponding interval, and B is the total number of grids in the study area, and FR represents the FR values of environmental factors.

2.3 C5.0 Decision Tree Model

The decision tree (DT) model is a concise prediction model, and it is also a very powerful and comprehensive machine learning algorithm (Gkioulekas and Papageorgiou 2021). It has been widely used in practical applications such as radar signal identification and classification, medical diagnosis, etc. The DT model is used to analyze and establish the most suitable label value of each pixel eigenvalue node, and then classify the data set according to the label value. The size of the data set has no influence on the size of the DT model, and a large number of data can be used to centrally construct decision trees. This study intends to use the C5.0 algorithm to construct a classification model, which is to conduct classification and regularization of a large number of data sets and then convey knowledge. Compared with other algorithms, it is more suitable for processing large data sets.

When the model is engaged in classification, each classifier is assigned a leaf and it is assigned to the class with the largest number. After the regression tree processing in the algorithm, the missing data is pruned and the classification tree reclassifies the data to generate the optimal binary tree. Then, the optimal segmentation value of the node is selected by citing the Gini coefficient of the economics category. The formula is:

$\omega \left(X\right)=\frac{Gini\left(X\right)}{{\displaystyle \sum _1^n{a}_i}}(2)$

where n is the number of branches, and a_i is the number of leaves when the branch is i.

2.4 Support Vector Machine Model

The SVM model increases the dimension of the independent variable on the basis of the kernel function. And it finds the optimal hyperplane with the largest category distance, then the support vector is used on the hyperplane to construct the model (Huang et al., 2020c). The input variables are linearly separable by transforming the nonlinear data into an n-dimensional hyperplane space based on a kernel function. Based on a set of linearly separable data, including 12 basic environmental factors and their corresponding output quantities $\left(x_i,y_i\right),i=\mathrm{1,2,3}\dots ,n$, the corresponding output categories are $y_i=\left\{\mathrm{0,1}\right\}$. Then the landslide inventory is distinguished by the maximum gap of the n-dimensional hyperplane space. Its formula is:

$y_i=\frac{1}{2}{\Vert \lambda \Vert }^2(3)$

For linear inseparable data, the slack variable ${\xi }_i$ is used to control the classification error, the constraint condition for correct classification is $y_i\left(\lambda x_i+b\right)\ge 1-{\xi }_i$, and the wrong classification is $y_i\left(\lambda x_i+b\right)\ge 1-{\xi }_i$, the distance formula of the hyperplane is

$L=\frac{1}{2}{\Vert \lambda \Vert }^2-\frac{1}{sn}{\displaystyle \sum _{i=1}^n{\delta }_i}(4)$

where ||λ||² is the norm of the normal hyperplane, and L is the Lagrange multiplier. In addition, the Radial basis kernel function is used as kernel function of SVM model.

2.5 Accuracy Evaluation of Landslide Susceptibility

The ROC curve is widely used in the overall accuracy evaluation of binary classification in the LSP modelling due to its satisfactory performance (Vakhshoori and Zare 2018). ROC curve is threshold independent curve. Its main advantage is that it is independent of the number and spacing of thresholds used for calculation (Fawcett 2006). Assuming n classes of the landslide susceptibility indexes, n+1 thresholds can be defined, where the first threshold value (i = 1) is lower than the minimum susceptibility index observed in the most stable category, and the last threshold value (i = n+1) is higher than the maximum susceptibility index in the most sensitive category. Each threshold forms a confusion matrix in which four types of pixels are defined: true positive (TP), false positive (FP), true negative (TN), and false negative (FN) pixels. According to the number of pixels in each threshold, two statistics can be calculated, namely TPR (true positive rate) and FPR (false positive rate), as shown as:

$TPR=\frac{TP}{TP+FN}(5)$

$FPR=\frac{FP}{TN+FP}(6)$

TPR and FPR are plotted on the Y-axis and X-axis of the ROC curve, respectively. Then the AUC is calculated as:

$AUC={\displaystyle {\sum }_{i=2}^{n+1}\frac{1}{2}\sqrt{{\left(x_i-x_{i+1}\right)}^2}•\left(y_i+y_{i+1}\right)}(7)$

The success rate of the model can be displayed through the participation of AUC values in the training data set, meanwhile, the prediction rate of the test data set can be displayed (Vakhshoori and Zare 2018).

3 Analysis of Study Area and Basic Environmental Factors

3.1 Introduction to the Study Area and Landslide Inventory

Ruijin is a city under the jurisdiction of Jiangxi Province (Figure 2). It is located on the west side of the southern end of the Wuyi Mountains. Ruijin City has a humid subtropical monsoon climate with concentrated rainfall in summer. Ruijin City is dominated by low mountains and hills, with an area of 1,967.85 km², which is 80.35% of the whole area. The main landform types include dissolved karst area, eroded moderate and low mountainous area, eroded and denuded hilly area, and river valley accumulation terraces. The rock in the study area is severely weathered, fault folds are developed, the territory is mountainous and rainy, and landslides are densely distributed, causing serious casualties and economic losses (Jiang and Huang 2016; Huang et al., 2020c).

FIGURE 2

FIGURE 2. The DEM and landslide catalog in Ruijin City.

According to the statistics of Disasters Prevention Departments, the cumulative number of landslides reached 370 by the end of 2014. The distribution area of these landslides and potential landslides are converted into 5515 landslide unit grids based on ArcGIS 10.3 software (Zhao Z. et al., 2021). Landslides mainly occur in the north and west of the study area, while landslides in the south and the central plain are sparse. The occurrence of landslides is characterized by the dense gullies and is concentrated in moderate terrain relief.

3.2 Landslide Data Sources

The relevant basic data sources in the study area are: 1) historical landslide inventory map of Ruijin City and relevant data recorded in the field. 2) The Digital Elevation Model data (DEM) is freely downloaded by the Earth Observation Center of the Chinese Academy of Sciences. Then, the DEM is processed through ArcGIS 10.3 software to obtain topographic factors such as slope, aspect, plane curvature, profile curvature, and terrain relief. 3) The lithology factor in the study area is obtained from China Hydrogeological map with 1:100,000 scale. 4) The Landsat-8 remote sensing images are freely downloaded by the Earth Observation Center of the Chinese Academy of Sciences. They are used to obtain surface coverage factors such as NDVI, NDBI, and MNDWI.

In addition, since the overall terrain relief in Ruijin City is moderate, and the territory is mostly hills, the 30 m resolution grid can well reflect the distribution of hills and plains (Ismail et al., 2016). Therefore, the landslide inventory selects a grid with 30 m resolution as the LSP unit. Since the data of the 366 landslides are not enough used as the input variables of the model (Huang et al., 2022c). Therefore, the “surface to raster” tool in ArcGIS 10.3 is further used to convert the 366 landslide areas into 5,515 landslide units. At the same time, the whole study area is divided into 2,750,691 units.

Finally, the grid units of the whole study area are classified into data layers representing environmental factors: 1) topographic and landform factors, including elevation, slope, aspect, plane curvature, profile curvature, terrain relief and gully density; 2) hydrology environmental factors, including terrain wetness index and MNDWI; 3) land cover factors, including NDVI and NDBI; 4) basic geotechnical factors, such as lithology.

3.3 Basic Environmental Factors and Frequency Ratio Analysis

The interaction of various environmental factors leads to landslide occurrence (Wang et al., 2014; Huang et al., 2016; Zheng et al., 2020). By studying the evolution characterization of the landslide plane in Ruijin City, 12 representative environmental factors, such as elevation, slope, terrain relief, NDVI, NDBI, are selected as the input variables of the C5.0 DT and SVM models. According to the natural break method, each environmental factor is divided into eight attribute interval levels, and the discrete lithology factor is classified according to the actual state. Thus, the FR values of the attribute intervals divided by FR method for each environmental factor are obtained, as shown in Table 1.

TABLE 1

TABLE 1. FR values of each environmental factors.

3.3.1 Topographic Factors

As shown in Table 1 and Figure 3A, when the elevation is between 139.7 and 308.4 m, the FR values are greater than 1, which indicates that the area is favorable for landslides occurrence. On the contrary, when the elevation is between 308.4 and 1117.8 m, the FR values are less than 1 or close to 1. This shows that the relationship between the attribute intervals of this environmental factor and the occurrence of landslides is weak. The areas with elevations between 308.5 and 1117.8 m are not contributed to landslide occurrence.

FIGURE 3

FIGURE 3. Environmental Factors: (A) Elevation, (B) Slope, (C) Aspect, (D) Plan curvature, (E) Profile curvature, (F) Lithology.

Both aspect and slope are also extracted from DEM data. Slope can effectively reflect the physical relationship between landslides and basic topographic elements (Huang et al., 2021a). As shown in Table 1, when the aspect is smaller than 202.5° or the slope is higher than 7.0°, landslides occurrence is more likely to happen. The plane curvature can reflect all the ridge lines and valley lines on the surface in the horizontal direction, and the profile curvature can describe the slope in the vertical direction, both of which are generally considered as important factors affecting landslides (Hong et al., 2019). As shown in Table 1, when the plane curvature is smaller than 27.8° and the profile curvature is in the range of 1.5°–8.7°, landslides are more likely to occur in this area.

Characteristic parameters such as gully density and terrain relief are also extracted from DEM, as shown in Figures 4B,C. The gully density expresses the degree of ground cutting and fragmentation, and local landforms play an important role in determining the susceptibility of landslides (Conforti and Ietto 2019). As shown in Table 1, when the gully density is between 0.8 and 1.6, it is more likely for landslides occurrence. Terrain relief is also one of the important factors that trigger landslides. It describes the undulation of the terrain surface (Qiu et al., 2017), which is prone to landslides at 29.6–150.8.

FIGURE 4

FIGURE 4. Environmental factors: (A) Terrain wetness index, (B) Gully density, (C) Terrain relief, (D) NDVI, (E) NDBI, (F) MNDWI.

3.3.2 Basic Geological Factors

Some studies have pointed out that lithology also plays an important role in affecting the occurrence of landslides (Conforti and Ietto 2019; Zhou et al., 2022). The evolution degree of cracks in soil layers of different lithology groups is quite different, and cracks have a great influence on the progressive failure process of soil landslides (Zheng et al., 2019a; Yang et al., 2020b; Jiang et al., 2020). This study adopts the latest geotechnical evaluation method, and analyzes the Mesoproterozoic Erathem (Pt2), Neoproterozoic Erathem (Pt3), Cambrian (∈), Ordovician (O), Silurian (S), Carboniferous (C), Permian (P), Triassic (T), Jurassic and Cretaceous (J、K), Quaternary (Q) and Waters (W) to improve the accuracy of LSP. As shown in Table 1, FR values are greater than 1 in the Pt2, Pt3, S, T, and Q lithology. Therefore, Pt2, Pt3, S, T, and Q lithology are prone to landslides.

3.3.3 Hydrological Environment and Land Cover Factors

The hydrological environment and the surface cover factors affect the shear strength of the soil. The decrease of the shear strength of the soil slope is also an important reason for the occurrence of landslides (Guo et al., 2019; Yang et al., 2019; Fu et al., 2020). The MNDWI and the terrain wetness index reflect the characterization of the surface moisture distribution in the study area, which can effectively indicate the influence of hydrological environmental factors on landslide susceptibility (Li Z.-L. et al., 2021). The FR values of MNDWI in nearly 90% of the units are positively correlated with the MNDWI, indicating that the water body has a great influence on the occurrence of landslides. As shown in Table 1, when the terrain wetness index is between 2.4 and 6.6, FR values are greater than 1, and the FR values are negatively correlated with the terrain wetness index.

NDVI usually reflects the coverage of local vegetation (Zhang H. et al., 2020), indicating that the occurrence of landslides is affected by vegetation coverage. Studies have shown that the spatial distributions of landslides and other geological hazards are highly correlated with human engineering activities such as road excavation and slope cutting (Zheng et al., 2018; Guo C. et al., 2021). NDBI is used to characterize the surface building density (Ridwan et al., 2021), which reflects the influence of human activities on landslide occurrence. As shown in Table 1, when NDVI is within 0.73–0.87 and NDBI is within 0.06–0.28, the FR values are greater than 1, and landslides are more likely to occur.

4. Results of Landslide Susceptibility Prediction Model

4.1 Landslide Susceptibility Predicted by C5.0 Decision Tree and SVM Models

Based on the 30 m resolution, the environmental factors in the study area are divided into 2,750,691 grids. The unit also ranks the main 10 basic environmental factors according to their contribution. The landslide interface will change continuously with the increase of the safety factor, meanwhile, its shear outlet will be directly or indirectly constrained. For complex landslides, directly intercepting the non-landslide area without considering the distribution of potential landslides does not meet the safety regulations for landslide control. Therefore, 366 landslide surfaces and potential landslide area that have occurred are taken and converted into 5,515 grid units.

Further, the susceptibility of the grid units extracted from the landslide area are assigned to 1, which is unstable areas. Meanwhile the same number of non-landslide grid units are randomly selected in the whole study area. The non-landslide grid units are assigned to 0, which are stable area. The assigned landslide and non-landslide susceptibility values are taken as the output variables of the LSP model, and then those landslide-non-landslide grid units are divided at a proportion of 7:3 in SPSS software, 70% of which are trained by SVM and C5.0 DT models, 30% are tested on SVM and C5.0 DT models.

Finally, the trained and tested SVM/C5.0 DT models are applied to the 12 environmental factors. Then, the LSM of the whole Ruijin City can be predicted. In ArcGIS 10.3 software, combined with the relationship between each environmental factor and the landslides, the predicted landslide susceptibility indexes are divided into five levels from low to high by using the natural break method (Kaur et al., 2018; Guo Z. et al., 2021).

4.2 Mapping Analysis of Landslide Susceptibility

The LSM based on the C5.0 DT model is shown in Figure 5A and Table 2. The predicted area proportions of the very low, low, moderate, high and very high landslide-prone area are respectively 54.75, 14.69, 3.34, 18.38, 8.82%. The FR values of very low, low, moderate, high and very high susceptible levels predicted by the C5.0 DT model are 0.07, 0.47, 0.92, 2.35, 4.88, respectively. The very high and high landslide-prone areas only accounted for 27.21% of the whole study area, but their corresponding FR values are contained for 86.29% of the total FR values.

FIGURE 5

FIGURE 5. LSM: (A) C5.0 DT, (B) SVM.

TABLE 2

TABLE 2. FR values of LSP grades in C5.0 DT and SVM models.

The LSM predicted by the SVM model is shown in Figure 5B and Table 2, and the predicted area proportions of the very low, low, moderate, high and very high landslide-prone areas are 30.78, 21.45, 16.59, 14.89 and 16.27%, respectively. The FR values of the predicted landslide susceptibility from very low to very high landslide-prone areas are 0.08, 0.34, 0.71, 1.47, and 3.47, respectively. While the very high and high landslide-prone areas only account for 31.17% of the whole study area, their corresponding FR values account for 78.42% of the total FR values.

Comparing the two models, the FR values increase along with the increase of the landslide susceptibility level. However, the proportion of FR values in high and very high susceptibility levels of C5.0 DT model are greater than that of SVM model. It shows that the LSP accuracy of the C5.0 DT model is higher than that of the SVM model, and that the landslide susceptibility indexes predicted by the C5.0 DT model are more consistent with the basic environmental factors of the study area.

As shown in Figure 5 and Table 2, most of Ruijin City is in the low and very low landslide-prone areas. The high and very high landslide-prone areas predicted by the two models are generally similar. But the high and very high landslide-prone areas predicted by the SVM model are significantly larger than C5.0 DT model, as a result, C5.0 DT model is better refined. And the high and very high landslide-prone areas are mainly concentrated in the north of the study area, while the high and very high landslide-prone areas in the south are sparse. The main reason distribution of landslides is that the terrain of Ruijin City is dominated by basins, surrounded by dense hills. The hills on the northern part of the city are the most dense, and the terrain is complex and the river network is concentrated. The weak surface slides down as a whole, which is conducive to the occurrence of landslides.

5 Discussion

This study discusses the LSP accuracy of the C5.0 DT and SVM models, the analysis of the basic environmental factors, and the application prospects of the LSP models.

5.1 ROC Curve for Landslide Susceptibility Predicted Accuracy of C5.0 Decision Tree and SVM Models

The prediction results of 70% of the training samples and 30% of the test samples of the C5.0 DT and SVM models are arranged from small to large, and are further used as the threshold for precision analysis in turn. The sample points greater than or equal to this threshold are defined as landslide points. The proportion of the correct landslide prediction points to the total number of landslide points is the model’s sensitivity which reflects the model’s ability to detect landslides; the proportion of the wrong landslide prediction points to the total number of non-landslide points is the model’s specificity which is reflects the model’s ability to correctly identify landslides (Paryani et al., 2021).

FPR is the value of 1-specificity; TPR is the value of sensitivity. The ROC curve mainly uses 1-specificity and sensitivity as the abscissa and ordinate, and its LSP accuracy results are shown in Figure 6. The AUC value of C5.0 DT is 0.886, while the AUC value of SVM is 0.819. The larger the AUC value, the better the overall prediction performance of the LSP model (Jiang et al., 2018a; Huang et al., 2021b; Paryani et al., 2021). Both C5.0 DT and SVM models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM.

FIGURE 6

FIGURE 6. ROC curves for LSP of C5.0 DT and SVM models.

5.2 Analysis of Environmental Factors

As shown in Figures 3, 4 and Table 1, for the basic environmental factors affecting the landslide susceptibility in Ruijin City. It is conducive to the occurrence of landslides, when the environmental factors are in these ranges where, the elevation is 139.7–308.5 m, the slope aspect is smaller than 202.5°, and the slope is high At 7.0°, the terrain wetness index is 2.5–6.6, the MNDWI is 0.39–0.62 or 0.74–0.86, the NDVI is 0.73–0.86, the NDBI is 0.16–0.27, and the lithology are Mesoproterozoic Pt2 (1), Late Paleoproterozoic (Z), Pt3, Silurian S, Triassic T, Quaternary Q. The influence degree of environmental factors on landslide susceptibility can more intuitively reflect the main environmental factors considered by different models and provide a theoretical reference for the LSP (Wu Y. et al., 2020; Chang et al., 2020; Zhao et al., 2020; Pan et al., 2021).

In this study, SPSS Modeler and R Studio software are used to analyze the main 10 environmental factors in the C5.0 DT and SVM models, and the importance of each factor is ranked. As shown in Figure 7, the environmental factors with higher importance in the SVM model are slope, lithology, elevation, gully density, terrain relief, etc. The environmental factors with a higher contribution degree considered by the C5.0 DT model are terrain relief, lithology, gully density, slope, etc. The top five environmental factors are the same for the C5.0 DT and SVM models, but the slope is more important in the SVM model, and the terrain relief is more important in the C5.0 DT model. This also leads to the difference in the LSP between C5.0 DT and SVM models. Comprehensive analysis shows that the LSP accuracy of the C5.0 DT model is greater than the results of the SVM model.

FIGURE 7

FIGURE 7. Relative importance of environmental factors, (A) C5.0 DT, (B) SVM.

5.3 Landslide Susceptibility Predicted Results of C5.0 Decision Tree and SVM Models and Their Application Prospects

Based on the analysis of the LSM and ROC curve in Ruijin City, the 366 landslides are concentrated in the high and very high susceptibility areas. Furthermore, the high and very high landslide areas are concentrated in the north and west of the study area. There are more landslides in the north and less in the south, more landslides in the edge and less in the middle. In the concentrated area of landslides, the altitude is low, the lithology formation is young, and the slope is relatively high. The obtained 12 types of basic environmental factors are representative. At the same time, the LSP of FR method based SVM and C5.0 DT is accurate, which have strong operability.

The reliability and LSP accuracy of the C5.0 DT model are higher than those of the SVM. The LSP result of C5.0 DT model can effectively give early warning to high landslide-prone area and reduce the occurrence of related safety accidents. Therefore, this study considers the C5.0 DT model to be an accurate and reliable LSP model. With the rapid development of big data platform, cloud computing, GIS and other technologies, machine learning have bright and broad application prospects in landslide susceptibility prediction (Dou et al., 2019; Merghadi et al., 2020). At the same time, C5.0 DT model is simple and clear, and has the advantages of easy learning, good reliability and high LSP accuracy (Abraham et al., 2021; Huang et al., 2021b).

6 Conclusion

This study explores the LSP results based on the FR method and C5.0 DT/SVM models in Ruijin City, and draws the following conclusions:

1) Both C5.0 DT and SVM models have high LSP accuracy, and the predicted landslide susceptibility maps are similar to each other on the whole. In addition, the landslides are mainly distributed in ravines and valleys. Especially, the landslides are likely to occur in the areas of moderate slope and large terrain relief.

2) The top contributing environmental factors calculated by the C5.0 DT/SVM models are terrain relief, lithology, gully density, slope and elevation. Factors that have minor effects on landslide susceptibility are the aspect, terrain wetness index, etc.

3) The C5.0 DT is an accurate and reliable LSP model. The LSP accuracy of C5.0 DT model is higher than that of the SVM model in LSP. It is concluded that we can predict accurate landslide susceptibility through combining the FR values and the C5.0 DT model.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

MS, AH, and JZ contributed to conception and design of the study. JZ, XC, and YT collected Ruijin City local landslide inventory. GL performed the statistical analysis. MS and AH wrote the first draft of the manuscript. MS, AH, JZ, and YT wrote sections of the manuscript. All authors contributed to manuscript revision, and approved the submitted version.

Funding

This research is funded by the Natural Science Foundation of China (No.52069013 and 52109089).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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