# Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

## 3. Methods

- (a)
- We construct a spatial database from various data sources and extract the landslide conditioning factors from the constructed database using two types of mapping units (raster and slope units).
- (b)
- We analyze the landslide conditioning factors through the optimize processes, which include multicollinearity diagnose and factor contribution analysis; then, the optimized factors are used to create the training and test datasets through resampling strategy.
- (c)
- We establish the susceptibility models using data-driven methods: logistic regression and random forest. The parameters of the involved machine learning methods are obtained by error and trial method. In addition, we assess and compare the models’ performance using some evaluation methods and an independent landslide dataset;
- (d)
- Lastly, we generate LSMs and comprehensively assess the overall performance of them. The main process is operated in ArcGIS.

#### 3.1. Construction of a Spatial Database

#### 3.1.1. Landslide Inventory

^{7}m

^{3}, and 207 earth slides [56], with volume ranging from 500 to 7 × 10

^{6}m

^{3}. However, landslides o medium (10

^{5}to 10

^{6}m

^{3}) to large-size (10

^{6}to 10

^{7}m

^{3}) account for 74% of the total landslides. A total of 50 landslides developed along the Yangtze River or its tributaries, which were considered to be seriously affected by groundwater level fluctuation. More than 90% percent of the recorded landslides were triggered or greatly affected by the intense rainfall which frequently occurred during rainy season. The main source of this landslide inventory is an old landslide inventory of the TGRA, supplemented with some recent reports of field investigations and landslide news.

#### 3.1.2. Landslide Conditioning Factors

#### 3.1.3. Mapping Unit

#### 3.1.4. Feature Selection Methods

^{2}measured the extent of one specific factor that is correlated with another factor in linear regression. The lower the standard error and VIF value, the lower the multicollinearity risk.

#### 3.2. Preparation of the Sample Datasets

#### 3.3. Landslide Susceptibility Models

#### 3.3.1. Logistic Regression

_{1}, x

_{2}, … x

_{n}are explanatory variables, and Y is a combination function that describe the linear relationship of these variables. For predicting the presence or absence of landslides, Y is used as a binary variable (0 or 1). The parameters b

_{1}, b

_{2}, … b

_{n}are the coefficients at normalized scale which allow for comparison of the relative importance of each independent variables on the response, and a is the intercept.

#### 3.3.2. Random Forest

#### 3.4. LSMs Performance and Validation

## 4. Results

#### 4.1. Selection of Landslide Conditioning Factor

#### 4.2. Validation and Model Comparison

#### 4.3. Producing LSMs and Result Evaluation

## 5. Discussion

#### 5.1. Conditioning Factors

#### 5.2. Scale Effects and Problem of Suitable Mapping Unit

#### 5.3. Model Comparison and Performance Evaluation

#### 5.4. Challenge and Future Directions

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Geological map of the study area, with location and landslide distribution of the transmission line corridor. The main strata include: T

_{1d}, T

_{1j}, T

_{2b}, T

_{3xj}: Triassic Daye Formation, Jialingjiang Formation, Badong Formation, and Xujiahe Formation, respectively; J

_{1}: Jurassic Zhenzhuchong Formation and Ziliujing Formation; J

_{2}: Jurassic Shaximiao Formation and Xingtiangou formation; J

_{3p}, J

_{3s}: Jurassic Penglaizhen Formation, and Suining Formation.

**Figure 3.**The ROC curves of the RF and LR models in landslide susceptibility assessment: (

**a**) training and (

**b**) testing.

**Figure 4.**Landslide Susceptibility Maps obtained with different models and mapping units. (

**a**) RF model in Raster unit. (

**b**) LR model in Raster unit. (

**c**) LR model in SU.

**Figure 5.**Distribution of recent landslides from 2016–2021 in generated landslide susceptibility maps, with 2 representative landslides showing in detail view: (

**a**) LSM by RF model in raster unit; (

**b**) LSM by LR model in SU.

**Figure 6.**The Yanzi ancient Landslides located at Badong County: (

**a**) general view of the Yanzi landslide, the yellow rectangles indicating the position of the tower before and after relocation. (

**b**–

**d**) Crack L1 on the foundation platform of the transmission tower; (

**e**) cracks in one leg of the transmission tower.

**Figure 7.**Landslide that occurred at Yunyang County in July 2020: (

**a**) general view of the landslide which posed threat to the tower No. 152. (

**b**) The aerial image of the landslide, where the landslide boundary is marked with yellow line.

Data Type | Data Source | Data Form | Data Scale |
---|---|---|---|

DEM | ASTER satellite | raster | 30 m |

Land cover | Chongqing Municipal Bureau of Land and Resources | raster | 30 m |

Geological map | National geological data museum | Vector | 1:200,000 |

Satellite image | Landsat-8 OLI data | raster | 30 m |

Administrative division | Geospatial Data Cloud platform | Vector | 1:100,000 |

Water system | Geospatial Data Cloud platform | Vector | 1:100,000 |

Road network | Geospatial Data Cloud platform | Vector | 1:100,000 |

Power transmission towers | China Electric Power Research Institute | Vector (Coordinate) | / |

Factors | Class | Classification Standard |
---|---|---|

Altitude (m) | 5 | 1. <400; 2. 400–600; 3. 600–800; 4. 800–1000; 5. >1000 |

Slope (°) | 5 | 1. <10°; 2. 10°~20°; 3. 20°~30°; 4. 30°~40°; 5. 40°~90°; |

Aspect | 9 | 1. Flat; 2. North; 3. Northeast; 4. East; 5. Southeast; 6. South; 7. Southwest; 8. West; 9. Northwest |

Profile curvature | 6 | 1. <−2; 2. −2~−1; 3. −1~0; 4. 0~1; 5. 1~2; 6. >2; |

Plan curvature | 6 | 1. <−2; 2. −2~−1; 3. −1~0; 4. 0~1; 5. 1~2; 6. >2; |

Lithology | 8 | 1.J_{3p}/J_{3sn}/J_{2x}; 2. J_{2s}/J_{2xs}; 3. J_{2q}/J_{1t}; 4. J_{1-2z}/J_{1zl}; 5. T_{3}-J_{1x}/T_{3xj}/T_{3j}; 6. T_{2b}; 7. T_{1d}/T_{1-2j}; 8.Q; |

Bedding Structure | 7 | 1.Horizontal strata slope; 2. Over-dip slope; 3. Under-dip slope; 4. Dip-oblique slope; 5. Transverse slope; 6. Anticlinal oblique slope; 7. Anticlinal slope; |

TRI | 6 | 1. 1.0~1.1; 2. 1.1~1.3; 3. 1.3~1.5; 4. 1.50~2.0; 6. >2; |

SPI | 7 | 1. 0~1; 2. 1~2; 3. 2~3; 4. 3~4; 5. 4~5; 6. 5~6; 7. >6; |

TWI | 5 | 1. 1.88~4.73; 2. 4.73~5.94; 3. 5.94~7.36; 4. 7.36~9.36; 5. 9.36~20.03; |

NDVI | 7 | 1. <0.10; 2. 0.10–0.20; 3. 0.20–0.30; 4. 0.30–0.40; 5. 0.40–0.50; 6. 0.50–0.60; 7. >0.60 |

Distance from rivers (m) | 6 | 1. <100; 2. 100~300; 3. 300~500; 4. 500~1000; 5. 1000~2000; 6. >2000; |

Land cover | 9 | 1.cropland; 2. Forest; 3. Grassland; 4. Shrub-land; 5. Wetland; 6. Water; 7. Tundra; 8. Impervious surface; 9. Bare land |

Distance from roads (m) | 6 | 1. <150; 2. 150~300; 3. 300~450; 4. 450~600; 5. 600~1000; 6. >1000; |

Distance from lineaments (m) | 6 | 1. <1000; 2. 1000~2000; 3. 2000~3000; 4. 3000~4000; 5. 4000~5000; 6. >5000; |

Mapping Unit | Numbers | Average Size (m^{2}) | Minimum Size (m^{2}) | Maximum Size (m^{2}) | Number of Landslide Units | Percentage of Landslide Units % |
---|---|---|---|---|---|---|

Slope Unit | 6735 | 215,764 | 5610 | 1,514,390 | 231 | 3.43 |

Grid cell Unit | 1,426,231 | 900 | 900 | 900 | 39, 190 | 2.75 |

**Table 4.**Multicollinearity analysis and factor contribution analysis result for each landslide conditioning factor in raster and SU form.

Conditioning Factors | Raster Unit | Slope Unit | |||||
---|---|---|---|---|---|---|---|

VIF | Tolerance | IGR | VIF | Tolerance | IGR | ||

1 | Altitude | 1.334 | 0.750 | 0.023 | 1.254 | 0.798 | 0.025 |

2 | TRI | 2.732 | 0.366 | 0.019 | 3.057 | 0.327 | 0.019 |

3 | Land cover | 1.265 | 0.790 | 0.019 | 1.126 | 0.888 | 0.043 |

4 | NDVI | 1.233 | 0.818 | 0.015 | 1.164 | 0.859 | 0.018 |

5 | Distance from rivers | 1.052 | 0.950 | 0.014 | 1.061 | 0.942 | 0.038 |

6 | TWI | 1.180 | 0.847 | 0.012 | / | / | / |

7 | Distance from roads | 1.214 | 0.824 | 0.012 | 1.161 | 0.861 | 0.019 |

8 | Slope | 2.941 | 0.340 | 0.012 | 2.211 | 0.452 | 0.022 |

9 | Lithology | 1.172 | 0.853 | 0.011 | 1.231 | 0.812 | 0.015 |

10 | SPI | 1.261 | 0.793 | 0.011 | / | / | / |

11 | Distance from lineaments | 1.095 | 0.913 | 0.010 | 1.064 | 0.939 | 0.014 |

12 | Plan curvature | 1.627 | 0.615 | 0.010 | 1.025 | 0.975 | 0.014 |

13 | Bedding Structure | 1.120 | 0.893 | 0.010 | 1.044 | 0.958 | 0.016 |

14 | Profile curvature | 1.515 | 0.660 | 0.010 | 1.220 | 0.819 | 0.016 |

15 | Aspect | 1.059 | 0.945 | 0.009 | 1.058 | 0.945 | 0.013 |

Model Stage | AUC | ACC | Precision | TPR | TNR | MCC | RMSE | MAE | |
---|---|---|---|---|---|---|---|---|---|

Training | RF (Raster) | 0.927 | 0.867 | 0.826 | 0.929 | 0.805 | 0.740 | 0.359 | 0.133 |

LR (Raster) | 0.846 | 0.771 | 0.762 | 0.787 | 0.756 | 0.543 | 0.478 | 0.229 | |

LR (SU) | 0.882 | 0.793 | 0.786 | 0.797 | 0.779 | 0.577 | 0.457 | 0.207 | |

Testing | RF (Raster) | 0.915 | 0.856 | 0.817 | 0.919 | 0.793 | 0.718 | 0.374 | 0.144 |

LR (Raster) | 0.839 | 0.766 | 0.759 | 0.781 | 0.751 | 0.532 | 0.484 | 0.234 | |

LR (SU) | 0.879 | 0.798 | 0.809 | 0.784 | 0.813 | 0.597 | 0.465 | 0.214 |

Susceptibility Level | Numbers of Landslides | Units in Landslide (A) | Units in Domain (B) | Proportion of Landslide in Total Landslide (C) | Proportion of Domain in Total Domain (D) | Proportion of Landslide in Domain (A/B) | |
---|---|---|---|---|---|---|---|

RF (Raster) | Low | 5 | 379 | 663,979 | 0.97% | 46.56% | 0.05% |

Moderate | 20 | 1568 | 374,940 | 4.00% | 26.29% | 0.37% | |

High | 76 | 13,140 | 276,480 | 33.53% | 19.39% | 4.24% | |

Very high | 164 | 24,103 | 110,832 | 61.50% | 7.75% | 19.39% | |

LR (Raster) | Low | 16 | 1627 | 584,948 | 4.15% | 41.01% | 0.28% |

Moderate | 32 | 5412 | 415,503 | 13.81% | 29.13% | 1.30% | |

High | 85 | 13,054 | 283,220 | 33.31% | 19.86% | 4.61% | |

Very high | 132 | 19,097 | 142,560 | 48.73% | 10.00% | 13.40% | |

LR (SU) | Low | 8 | 8 | 2670 | 3.46% | 39.64% | 0.30% |

Moderate | 43 | 38 | 2036 | 16.45% | 30.23% | 1.87% | |

High | 86 | 82 | 1357 | 35.50% | 20.15% | 6.04% | |

Very high | 128 | 103 | 672 | 44.59% | 9.98% | 15.32% |

Low | Moderate | High | Very High | |
---|---|---|---|---|

RF | 1 | 3 | 6 | 4 |

LR (SU) | 1 | 3 | 4 | 6 |

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## Share and Cite

**MDPI and ACS Style**

Liu, S.; Yin, K.; Zhou, C.; Gui, L.; Liang, X.; Lin, W.; Zhao, B.
Susceptibility Assessment for Landslide Initiated along Power Transmission Lines. *Remote Sens.* **2021**, *13*, 5068.
https://doi.org/10.3390/rs13245068

**AMA Style**

Liu S, Yin K, Zhou C, Gui L, Liang X, Lin W, Zhao B.
Susceptibility Assessment for Landslide Initiated along Power Transmission Lines. *Remote Sensing*. 2021; 13(24):5068.
https://doi.org/10.3390/rs13245068

**Chicago/Turabian Style**

Liu, Shuhao, Kunlong Yin, Chao Zhou, Lei Gui, Xin Liang, Wei Lin, and Binbin Zhao.
2021. "Susceptibility Assessment for Landslide Initiated along Power Transmission Lines" *Remote Sensing* 13, no. 24: 5068.
https://doi.org/10.3390/rs13245068