# Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

^{1}

^{2}

^{3}

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^{5}

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Materials

#### 2.1. Description of the Study Area

^{2}. Figure 1 shows the location of Ludian County. The mean annual temperature and rainfall are about 12.1 °C and 923.5 mm, respectively. The terrain in Ludian County is rather steep and comprises two mountains and several dissected ravines [34]. Ludian County is considered a seismic zone. In fact, there have been more than 15 earthquakes with magnitudes of Mw 6.0 or larger in Ludian since 1900 [35]. Thus, landslides induced by earthquakes pose high risks to the people living in the area. Figure 2 shows the typical earthquake-induced landslide disasters in the Ludian earthquake zone.

#### 2.2. Landslide Inventory Mapping

#### 2.3. Landslide Conditioning Factors

#### 2.3.1. Topographic Factors

^{2}, 88% of which is mountainous areas.

#### 2.3.2. Geological Factors

#### 2.3.3. Land Use and Land Cover Factors

#### 2.3.4. Hydrological Factors

^{2}·m

^{−1}). Figure 3k shows that the TWI is classified into six groups: <−17, −17~−15, −15~−12, −12~−8, −8~−3, and −2–11.

#### 2.3.5. Geophysical Factor

## 3. Methodology

#### 3.1. Conditioning Factors Analysis

#### 3.1.1. Multicollinearity Analysis

#### 3.1.2. Frequency Ratio Method

#### 3.2. Convolutional Neural Networks

#### 3.3. Proposed Model

#### 3.4. Evaluation and Comparison Methods

#### 3.5. Sensitivity Analysis of Conditioning Factors

_{all}and AUC

_{i}represent the AUC computed from the prediction using all the fac-tors and the prediction without using the ith factor, respectively.

## 4. Results

#### 4.1. Selection and Analysis of the Landslide Conditioning Factors

#### 4.2. Construction of Proposed Model

#### 4.3. Generation of Landslide Susceptibility Maps

#### 4.4. Evaluation and Comparison of Results

#### 4.5. Sensitivity Analysis Results

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Slope failure, (

**b**) debris flow, (

**c**) rock fall, and (

**d**) ground fissures after the 3 August 2014 earthquake event.

**Figure 3.**Landslide conditioning factors. (

**a**–

**e**) Topographic factors: (

**a**) elevation, (

**b**) slope, (

**c**) aspect, (

**d**) relief, and (

**e**) curvature); (

**f**,

**g**) geological factors: (

**f**) lithology and (

**g**) the distances to faults; (

**h**–

**j**) land use and land cover factors: (

**h**) land use, (

**i**) NDVI, and (

**j**) the distances to roads; (

**k**–

**m**) hydrological factors: (

**k**) TWI, (

**l**) SPI, and (

**m**) the distances to rivers; and the (

**n**) geophysical factor: PGA.

**Figure 6.**Relationship between the landslides and the conditioning factors: (

**a**) Elevation (m), (

**b**) Slope (°), (

**c**) Aspect, (

**d**) Relief (m), (

**e**) Curvature, (

**f**) Lithology, (

**g**) the distances to the faults (m), (

**h**) land use, (

**i**) the NDVI, (

**j**) the distances to the roads (m), (

**k**) TWI, (

**l**) SPI, (

**m**) the distances to the rivers (m), and (

**n**) the PGA.

**Figure 8.**Landslide susceptibility maps using (

**a**) a hybrid model, (

**b**) CNN-2D, (

**c**) CNN-1D, (

**d**) RF, and (

**e**) SVM.

**Figure 9.**Landslide density of the various susceptibility classes of LSM generated by the proposed model.

**Figure 10.**The receiver operating characteristic (ROC) curves on the validation set (with a confusion matrix where the TN, FP, FN, and TP denote the true negative, false positive, false negative, and true positive, respectively).

Conditioning Factors | Collinearity Statistics | |
---|---|---|

Tolerance | VIF | |

Aspect | 0.967 | 1.034 |

Curvature | 0.892 | 1.121 |

Elevation | 0.548 | 1.823 |

Distance to faults | 0.92 | 1.087 |

Land use | 0.787 | 1.271 |

Lithology | 0.829 | 1.206 |

NDVI | 0.741 | 1.35 |

PGA | 0.806 | 1.241 |

Relief | 0.32 | 3.124 |

Distance to rivers | 0.566 | 1.766 |

Distance to roads | 0.74 | 1.352 |

Slope | 0.287 | 3.488 |

SPI | 0.733 | 1.365 |

TWI | 0.584 | 1.712 |

No. | Parameters | Values |
---|---|---|

1 | Conventional kernel size (1D) | (3, 3) |

2 | Conventional kernel size (2D) | (3, 3) |

3 | Pooling size (2D) | (2, 2) |

4 | Loss function | Cross entropy |

5 | Optimizer | Adagrad |

6 | Epoch | 300 |

7 | Batch size | 32 |

8 | Learning rate | 0.08 |

9 | Activation function | ReLU |

Classes | Hybrid | CNN-2D | CNN-1D | RF | SVM |
---|---|---|---|---|---|

Very low | 611.7 (38.1%) | 552.81 (34.5%) | 741.69 (46.2%) | 294 (18.3%) | 377.93 (22.6%) |

Low | 102.69 (6.4%) | 176.45 (11.0%) | 35.99 (2.2%) | 356.09 (22.2%) | 293.84 (18.3%) |

Moderate | 80.43 (5.0%) | 145.57 (9.1%) | 23.37 (1.5%) | 338.2 (21.1%) | 340.86 (21.3%) |

High | 101.91 (6.3%) | 172.21 (10.7%) | 78.34 (4.9%) | 320.72 (20.0%) | 313.56 (19.6%) |

Very high | 706.95 (44.1%) | 556.62 (34.7%) | 724.29 (45.2%) | 294.66 (18.4%) | 227.49 (17.3%) |

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**MDPI and ACS Style**

Yang, X.; Liu, R.; Yang, M.; Chen, J.; Liu, T.; Yang, Y.; Chen, W.; Wang, Y.
Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. *Remote Sens.* **2021**, *13*, 2166.
https://doi.org/10.3390/rs13112166

**AMA Style**

Yang X, Liu R, Yang M, Chen J, Liu T, Yang Y, Chen W, Wang Y.
Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. *Remote Sensing*. 2021; 13(11):2166.
https://doi.org/10.3390/rs13112166

**Chicago/Turabian Style**

Yang, Xin, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, and Yuting Wang.
2021. "Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping" *Remote Sensing* 13, no. 11: 2166.
https://doi.org/10.3390/rs13112166