# Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake

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## Abstract

**:**

## 1. Introduction

## 2. Geological Setting and Landslide Inventory of Palu Earthquake

#### 2.1. Geological Setting

#### 2.2. Landslide Inventory of the Palu Earthquake

^{2}. The landslides observed in this study predominantly comprise shallow landslides, along with a smaller number of large-scale liquefaction-induced flowslides, debris flows, and rockslides. The majority of these landslides are distributed within the peak ground acceleration (PGA) range of 0.3 g (Figure 2). The total area affected by seismic landslides was approximately 43.0 km

^{2}, with an average area of 2700 m

^{2}. Notably, the Petobo landslide stood out as the largest, covering an extensive area of approximately 1.97 km

^{2}, while the smallest landslide measured as little as 45 m

^{2}(Figure 2). The majority of landslides fell within the range of 500–2500 m

^{2}, constituting around 60% of the total amount (Figure 2). Moreover, 414 landslides encompassed an individual area exceeding 10,000 m

^{2}, and a total of 1393 landslides are greater than 5000 m

^{2}[4]. The results indicate that the coseismic landslides are primarily influenced by the seismogenic fault. Furthermore, landslides exhibit a strong correlation with several factors, including elevation, slope angle, aspect, rock type, and peak ground acceleration (PGA). Landslide occurrence increases with greater slope angles, PGA, and topographic relief [4].

## 3. Data and Methods

#### 3.1. Data Sources

#### 3.2. Method

## 4. Results

## 5. Discussion

#### 5.1. Converting LSI to Landslide Percentage (Lp)

#### 5.2. Relative Importance of the Influencing Factors

#### 5.3. Comparisons of the RF Model with LR Model

## 6. Conclusions

- (1)
- Based on the LSM predicted by two models and actual landslides, the landslide abundance area roughly matches the area of high LSI, with areas with LSI mainly concentrated along both sides of the seismogenic fault. The areas with high LSI mainly include the southern part of the epicenter and the areas on both sides of the Palu basin, which are also the landslide abundance areas.
- (2)
- Compared to the LR model, the std of the RF model is smaller, with a max std of 0.13. The std based on the RF model is lower than that of the LR model, indicating that the evaluation results based on the RF model are less affected by the changes in training samples, while the predicted result of the LR model has a relatively large variation in LSI with the changes in training samples.
- (3)
- The assessment results based on the RF model are less affected by the changes in the training samples, while the predicted result of the LR model has a relatively large variation in LSI with the changes in the training samples. Both models demonstrate satisfactory performance; the RF model exhibits higher predictive capability compared to the LR model. The RF model, with a predicted rate of 0.94, is significantly higher than the rate of 0.86 for the LR model. Overall, the LR and RF models are useful tools for LSM of seismic events.
- (4)
- We calculate the probability of landslide occurrence and average LSI for each interval using 0.05 width bins, and then fit the relationship between LSI and landslide percentage (Lp). The results indicate that there is a clear exponential relationship between the LSI and the landslide percentage (Lp) of ${L}_{p}=0.0134\ast \mathrm{e}\mathrm{x}\mathrm{p}(6.1048\ast LSI)$ for the LR model and ${L}_{p}=0.0078\ast \mathrm{e}\mathrm{x}\mathrm{p}(7.0362\ast LSI)$ for the RF model. This equation can be used to correct the LSI to represent the landslide percentage (Lp) when the 1:1 ratio of landsliding/non-landsliding is used for modelling of the Palu area.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Mapping showing the distribution of aftershocks, topography, and structural features of the 2018 Mw 7.5 Palu earthquake.

**Figure 3.**Map showing the influencing factors used for the landslide susceptibility modelling; (

**a**) Hillslope gradient and drainages distribution; (

**b**) Topographic relief; (

**c**) Aspect; (

**d**) Peak ground acceleration (PGA); (

**e**) Peak ground velocity (PGV); (

**f**) Lithology.

**Figure 5.**Map showing the mean landslide susceptibility index (LSI) and model uncertainty for the LR model; (

**a**) the result of mean LSI yielding 20 predicted pictures; (

**b**) The result of standard deviation (std) for model uncertainty. The red polygons represent the coseismic landslides.

**Figure 6.**Map showing the mean landslide susceptibility index (LSI) and model uncertainty for the RF model; (

**a**) the result of mean LSI yielding 20 predicted pictures; (

**b**) The result of standard deviation (std) for model uncertainty.

**Figure 7.**Scatter plot showing the relationship between the average LSI and standard deviation (std) of the two different models. (

**a**) LR model; (

**b**) RF model. The circles represent the grid cells.

**Figure 8.**Map showing the frequency density distribution of LSI for two different models; (

**a**) LR model; (

**b**) RF model.

**Figure 10.**The statistical results of the predicted area and the landslide number density (LND) distribution under LSI class for two models.

**Figure 11.**Prediction curves of 20 maps of landslide susceptibility mapping using two models. (

**a**) LR model; (

**b**) RF model.

**Figure 12.**Map showing the relationship of LSI between LR-based and RF-based landslide susceptibility assessment results.

**Figure 13.**Relationship between LSI and landslide percentage used to represent the actual occurrence probability of the landslide.

**Figure 14.**Map showing the relative importance of the continuous variables for two models; the red and blue boxes represent the LR and RF models, respectively.

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**MDPI and ACS Style**

Ma, S.; Shao, X.; Xu, C.
Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake. *Remote Sens.* **2023**, *15*, 4733.
https://doi.org/10.3390/rs15194733

**AMA Style**

Ma S, Shao X, Xu C.
Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake. *Remote Sensing*. 2023; 15(19):4733.
https://doi.org/10.3390/rs15194733

**Chicago/Turabian Style**

Ma, Siyuan, Xiaoyi Shao, and Chong Xu.
2023. "Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake" *Remote Sensing* 15, no. 19: 4733.
https://doi.org/10.3390/rs15194733