# Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Materials

#### 2.1. Study Area

^{2}and includes 17 counties and cities. Liangshan belongs to the subtropical monsoon climate zone (Huang et al., 2014). The ranges for annual average precipitation, temperature, and sunshine duration are 748.5–1185.0 mm, 10.6–19.2 °C, and 1038.0–2611.4 h per year, respectively.

#### 2.2. Preparation of Sample Dataset

^{3}), medium-sized landslides (105~106 m

^{3}), and large and other landslides (>106 m

^{3}) accounted for 68%, 27%, and 5% of the total, respectively. The causes of landslides are missing in our data. Therefore, we investigated the general causes triggering landslides in this area via, e.g., learning from the official website of the local government, saying that precipitation and human activities are the main causes of landslides.

#### 2.3. Landslide Conditioning Factors

#### 2.3.1. Topography Factors

#### 2.3.2. Geology Factors

#### 2.3.3. Ecology Factors

#### 2.3.4. Hydrology and Meteorology Factors

#### 2.3.5. Human Activity Factors

## 3. Methodology

#### 3.1. Feature Screening Method

#### 3.2. Machine Learning Models

#### 3.2.1. Random Forest

#### 3.2.2. Geographical Random Forest

#### 3.3. Model Evaluation Metrics and Validation Methods

## 4. Results

#### 4.1. Multicollinearity Diagnosis

#### 4.2. Model Performance Evaluation

#### 4.3. Global Feature Importance Comparison

#### 4.4. Landslide Susceptibility Maps

## 5. Discussion

#### 5.1. Local Feature Importance

#### 5.2. Limitations

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Landslide conditioning factors: (

**a**) elevation; (

**b**) slope angle; (

**c**) slope aspect; (

**d**) profile curvature; (

**e**) plan curvature; (

**f**) topographic relief; (

**g**) TRI; (

**h**) lithology; (

**i**) distance to faults; (

**j**) soil type; (

**k**) NDVI; (

**l**) TWI; (

**m**) SPI; (

**n**) STI; (

**o**) distance to rivers; (

**p**) average annual rainfall; (

**q**) distance to roads; (

**r**) land use.

**Figure 7.**Local feature importance at the county level: (

**a**) Butuo County; (

**b**) Dechang County; (

**c**) Ganluo County; (

**d**) Huidong County; (

**e**) Huili City; (

**f**) Jinyang County; (

**g**) Leibo County; (

**h**) Meigu County; (

**i**) Mianning County; (

**j**) Muli County; (

**k**) Ningnan County; (

**l**) Puge County; (

**m**) Xichang City; (

**n**) Xide County; (

**o**) Yanyuan County; (

**p**) Yuexi County; (

**q**) Zhaojue County.

**Figure 8.**The four most important features at the county level: (

**a**) first feature ranking; (

**b**) second feature ranking; (

**c**) third feature ranking; (

**d**) fourth feature ranking.

Category | Subcategories | Applicable Scale |
---|---|---|

Knowledge-driven methods | - | Small (<1:250,000), Medium (1:250,000–1:25,000) |

Data-driven methods | Deterministic methods | Large (1:25,000–1:5000), Detailed (<1:5000) |

Traditional statistical methods | Medium (1:250,000–1:25,000), Large (1:25,000–1:5000), Detailed (<1:5000) | |

Machine learning methods | Large (1:25,000–1:5000), Detailed (<1:5000) |

Category | Conditioning Factor | Data Name | Data Source | Data Type | Precision |
---|---|---|---|---|---|

Topography factors | Elevation | DEM | Geospatial Data Cloud | Grid | 30 m |

Slope angle | |||||

Slope aspect | |||||

Profile curvature | |||||

Plan curvature | |||||

Topographic relief | |||||

Topographic roughness index | |||||

Geology factors | Lithology | Geologic map | RESDC | Vector | 1:200,000 |

Distance to faults | |||||

Ecology factors | Soil type | Soil map | Grid | 30 m | |

Normalized difference vegetation index | Landsat8 | United States Geological Survey | Grid | 30 m | |

Hydrology and meteorology factors | Topographic wetness index | DEM | Geospatial Data Cloud | Grid | 30 m |

Stream power index | |||||

Sediment transport index | |||||

Distance to rivers | River map | National Basic Geographical Database (NBGD) | Vector | 1:250,000 | |

Average annual rainfall | Rainfall monitoring data | China Meteorological Data Network | Tabular | - | |

Human activity factors | Distance to roads | Road map | NBGD | Vector | 1:250,000 |

Land use | Land use map | RESDC | Grid | 30 m |

Category | Factor | Classification Scheme |
---|---|---|

Topography factors | Elevation (m) | 1. <1500; 2. 1500–2000; 3. 2000–2500; 4. 2500–3000; 5. 3000–3500; 6. 3500–4000; 7. >4000 |

Slope angle (°) | 1. <10; 2. 10–20; 3. 20–30; 4. 30–40; 5. 40–50; 6. >50 | |

Slope aspect | 1. flat; 2. north; 3. northeast; 4. east; 5. southeast; 6. south; 7. southwest; 8. west; 9. northwest | |

Profile curvature | 1. <−3; 2. −3 to −2; 3. −2 to −1; 4. −1 to 0; 5. 0 to 1; 6. 1 to 2; 7. 2 to 3; 8. >3 | |

Plan curvature | 1. <−2; 2. −2 to −1; 3. −1 to −0.5; 4. −0.5 to 0; 5. 0 to 0.5; 6. 0.5 to 1; 7. 1 to 2; 8. >2 | |

Topographic relief (m) | 1. <20; 2. 20–35; 3. 35–50; 4. 50–65; 5. 65–80; 6. 80–110; 7. >110 | |

Topographic roughness index | 1. <1.05; 2. 1.05–1.25; 3. 1.25–1.5; 4. 1.5–2; 5. >2 | |

Geology factors | Lithology | 1. extremely soft rock; 2. soft rock; 3. soft–hard combined rock; 4. hard rock; 5. extremely hard rock |

Distance to faults (m) | 1. <500; 2. 500–1000; 3. 1000–1500; 4. 1500–2000; 5. 2000–2500; 6. 2500–3000; 7. >3000 | |

Ecology factors | Soil type | 1. semi-leaching soil; 2. semi-hydraulic soil; 3. primary soil; 4. alpine soil; 5. lake and water; 6. leaching soil; 7. anthropogenic soil; 8. water-forming soil; 9. iron-bauxite; 10. rock |

Normalized difference vegetation index | 1. <0; 2. 0–0.2; 3. 0.2–0.4; 4. 0.4–0.6; 5. 0.6–0.8; 6. >0.8 | |

Hydrology and meteorology factors | Topographic wetness index | 1. <4; 2. 4–6; 3. 6–8; 4. 8–11; 5. 11–15; 6. 15–21; 7. >21 |

Stream power index | 1. <15; 2. 15–30; 3. 30–45; 4. 45–60; 5. 60–100; 6. 100–1000; 7. >1000 | |

Sediment transport index | 1. <20; 2. 20–40; 3. 40–70; 4. 70–100; 5.100–200; 6. >200 | |

Distance to rivers (m) | 1. <200; 2. 200–400; 3. 400–600; 4. 600–800; 5. 800–1000; 6. 1000–1200; 7. >1200 | |

Average annual rainfall (mm) | 1. <785; 2. 785–843; 3. 843–892; 4. 892–928; 5. 928–971; 6. 971–1053; 7. >1053 | |

Human activity factors | Distance to roads (m) | 1. <200; 2. 200–400; 3. 400–600; 4. 600–800; 5. 800–1000; 6. 1000–1200; 7. >1200 |

Land use | 1. cropland; 2. forest land 3. grassland; 4. water area; 5. construction land; 6. unused land |

Conditioning Factors | TOL | VIF | Conditioning Factors | TOL | VIF |
---|---|---|---|---|---|

Elevation | 0.59 | 1.70 | Soil type | 0.82 | 1.23 |

Slope angle | 0.15 | 6.56 | NDVI | 0.87 | 1.16 |

Slope aspect | 0.99 | 1.01 | TWI | 0.49 | 2.06 |

Profile curvature | 0.77 | 1.30 | SPI | 0.31 | 3.26 |

Plan curvature | 0.60 | 1.67 | STI | 0.35 | 2.83 |

Topographic relief | 0.17 | 5.74 | Distance to rivers | 0.86 | 1.16 |

TRI | 0.23 | 4.31 | Average annual rainfall | 0.80 | 1.25 |

Lithology | 0.91 | 1.10 | Distance to roads | 0.87 | 1.14 |

Distance to faults | 0.95 | 1.05 | Land use | 0.94 | 1.06 |

Model | Cross-Validation Method | Accuracy | Precision | Recall | F-Score | AUC |
---|---|---|---|---|---|---|

RF | Random CV | 0.806 | 0.781 | 0.852 | 0.815 | 0.876 |

GRF | 0.829 | 0.801 | 0.879 | 0.838 | 0.881 | |

RF | Spatial CV | 0.789 | 0.766 | 0.833 | 0.798 | 0.856 |

GRF | 0.798 | 0.767 | 0.858 | 0.810 | 0.860 |

Models | Susceptibility Level | Area (km^{2}) | Percentage of Area (%) | Number of Landslides | Percentage of Landslides (%) | Landslide Density |
---|---|---|---|---|---|---|

RF | Very Low | 26,450.31 | 43.86 | 12 | 0.52 | 0.01 |

Low | 10,132.89 | 16.80 | 61 | 2.64 | 0.16 | |

Moderate | 8327.19 | 13.81 | 184 | 7.96 | 0.58 | |

High | 8239.20 | 13.66 | 469 | 20.29 | 1.48 | |

Very High | 7154.91 | 11.86 | 1586 | 68.60 | 5.78 | |

GRF | Very Low | 24,289.59 | 40.28 | 8 | 0.35 | 0.01 |

Low | 11,706.39 | 19.41 | 31 | 1.34 | 0.07 | |

Moderate | 8964.90 | 14.87 | 138 | 5.97 | 0.40 | |

High | 8349.93 | 13.85 | 411 | 17.78 | 1.28 | |

Very High | 6993.69 | 11.60 | 1724 | 74.57 | 6.43 |

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## Share and Cite

**MDPI and ACS Style**

Dai, X.; Zhu, Y.; Sun, K.; Zou, Q.; Zhao, S.; Li, W.; Hu, L.; Wang, S.
Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China. *Remote Sens.* **2023**, *15*, 1513.
https://doi.org/10.3390/rs15061513

**AMA Style**

Dai X, Zhu Y, Sun K, Zou Q, Zhao S, Li W, Hu L, Wang S.
Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China. *Remote Sensing*. 2023; 15(6):1513.
https://doi.org/10.3390/rs15061513

**Chicago/Turabian Style**

Dai, Xiaoliang, Yunqiang Zhu, Kai Sun, Qiang Zou, Shen Zhao, Weirong Li, Lei Hu, and Shu Wang.
2023. "Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China" *Remote Sensing* 15, no. 6: 1513.
https://doi.org/10.3390/rs15061513