# Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

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## Abstract

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## 1. Introduction

## 2. Study Area and Data

## 3. Methodology

#### 3.1. Model Training Algorithm

#### 3.2. Over-Sampling and Under-Sampling

#### 3.3. Model Evaluation

#### 3.4. Data Processing

## 4. Results

#### 4.1. Consequences of Landslide Sensitivity Prediction

#### 4.2. Validation and Comparison of Models

## 5. Discussion

#### 5.1. Limitations or Shortcomings of This Study

- Only one unbalanced landslide dataset was used in this study, but no additional high-quality unbalanced datasets were collected for the experiments, which may limit the generalizability of the results.
- In this study, we trained three models using an unbalanced dataset and six models using a balanced dataset. With some metrics, we can visually compare the performance strengths and weaknesses of the models obtained from the training of these two datasets. However, we failed to use a suitable comprehensive metric to compare the two models, just as one cannot use the same set of rules to compare different things. This is a limitation of this study, and future research needs to explore more comprehensive metrics to evaluate the performance of the models.
- It was found that models trained on an unbalanced dataset and models trained on a downsampled balanced dataset achieved similar values for several evaluation metrics, suggesting the need to investigate the relationship between the two models in greater depth.
- All three algorithms chosen for this study are classical machine learning algorithms because they are well-interpretable compared to neural-network-based algorithms, such as Deep Residual Shrinkage Network [57] and Squeeze-and-Excitation Network [58] (SENet). Neural network algorithms change too much and are not reproducible during the learning training process, and even with the same environment and parameters, the models obtained the next time are often very different. However, the main idea of this study is to use the control variables method to highlight the influence of the dataset on the model, so the neural network-based algorithm is not applicable to this study. Nevertheless, future research could explore the possibility of using neural network algorithms in similar studies, which would help to extend the range of algorithm choices and improve the performance of the models.

#### 5.2. Future Research Directions

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location map of the study area. Part A shows the remote sensing image of the study area, and part B shows the geographical location of the study area.

Raw Data | Type | Source |
---|---|---|

Historic landslide | Vector | geological survey and remote sensing images |

DEM | Raster | Aster GDEM (https://earthdata.nasa.gov/) |

Landsat 8 OLI | Raster | USGS (https://earthexplorer.usgs.gov/) |

Lithology | Vector | local Land and Resources Bureau |

Meteorological data | Vector | Meteorological Bureau (http://www.cma.gov.cn/) |

Variables | Name | Variable Type | Classification |
---|---|---|---|

Y | Landslide | Binary | Landslide |

X1 | Elevation | Continuous | Topography |

X2 | Slope | Continuous | Topography |

X3 | Aspect | Discrete | Topography |

X4 | Curvature | Continuous | Topography |

X5 | Distance to river | Continuous | Hydrology |

X6 | NDVI | Continuous | Land cover |

X7 | NDWI | Continuous | Land cover |

X8 | Rainfall | Discrete | Triggered |

X9 | Seismic intensity | Discrete | Triggered |

X10 | Land use | Discrete | Triggered |

X11 | TRI | Continuous | Topography |

X12 | Lithology | Continuous | Topography |

Algorithm | Advantages | Disadvantages |
---|---|---|

LightGBM | High accuracy and efficiency | Prone to overfitting with small datasets |

Handles large datasets well | Requires careful tuning of hyperparameters | |

Can handle missing values | Less interpretable compared to simpler models | |

Random Forest | High accuracy and robustness | Can be computationally expensive with large datasets |

Handles high-dimensional data well | Limited interpretability compared to simpler models | |

Can handle missing values and categorical features | May not perform well with imbalanced datasets | |

Logistic Regression | Simple and interpretable | May not perform well with nonlinear relationships |

Fast and efficient | ||

Performs well with small datasets | Can be sensitive to outliers and influential observations |

Variables | Name | Variance Inflation Factor (VIF) |
---|---|---|

X1 | Elevation | 2.4049 |

X2 | Slope | 9.6671 |

X3 | Aspect | 1.0288 |

X4 | TRI | 9.3899 |

X5 | Curvature | 1.0156 |

X6 | Lithology | 1.6362 |

X7 | River | 1.9169 |

X8 | NDVI | 2.1679 |

X9 | NDWI | 1.4324 |

X10 | Rainfall | 1.4463 |

X11 | Earthquake | 1.6907 |

X12 | Land_use | 1.6336 |

**Table 5.**Recall, Accuracy, mean Average Precision, G-mean, F1_score, Precision and AUC of the nine models.

Models | mAP | G-Mean | Recall | Accuracy | F1_Score | Precision | AUC |
---|---|---|---|---|---|---|---|

LR | 0.5 | 0 | 0 | 0.95 | 0 | 0 | 0.801 |

U_LR | 0.765 | 0.764 | 0.815 | 0.72 | 0.227 | 0.132 | 0.824 |

O_LR | 0.772 | 0.771 | 0.803 | 0.744 | 0.24 | 0.141 | 0.835 |

RF | 0.5 | 0.016 | 0 | 0.95 | 0.001 | 0.158 | 0.79 |

U_RF | 0.682 | 0.666 | 0.537 | 0.813 | 0.224 | 0.142 | 0.79 |

O_RF | 0.811 | 0.804 | 0.705 | 0.907 | 0.432 | 0.311 | 0.932 |

LGRB | 0.501 | 0.046 | 0.002 | 0.949 | 0.004 | 0.169 | 0.797 |

U_LGBM | 0.729 | 0.729 | 0.71 | 0.746 | 0.219 | 0.13 | 0.797 |

O_LGBM | 0.81 | 0.81 | 0.826 | 0.796 | 0.29 | 0.176 | 0.882 |

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## Share and Cite

**MDPI and ACS Style**

Song, Y.; Yang, D.; Wu, W.; Zhang, X.; Zhou, J.; Tian, Z.; Wang, C.; Song, Y.
Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models. *ISPRS Int. J. Geo-Inf.* **2023**, *12*, 197.
https://doi.org/10.3390/ijgi12050197

**AMA Style**

Song Y, Yang D, Wu W, Zhang X, Zhou J, Tian Z, Wang C, Song Y.
Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models. *ISPRS International Journal of Geo-Information*. 2023; 12(5):197.
https://doi.org/10.3390/ijgi12050197

**Chicago/Turabian Style**

Song, Yingze, Degang Yang, Weicheng Wu, Xin Zhang, Jie Zhou, Zhaoxu Tian, Chencan Wang, and Yingxu Song.
2023. "Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models" *ISPRS International Journal of Geo-Information* 12, no. 5: 197.
https://doi.org/10.3390/ijgi12050197