# Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}, population 4219, 1914 households) and Jumunjin-eup (area 60.55 km

^{2}, population 21,291, 8917 households) are areas where many landslides have occurred due to typhoons Rusa and Mica. In particular, seven landslides of Gangneung city including the study area were cut off by a landslide caused by typhoon Rusa in 2002, and three residents of Samjyunjin-eup were killed and many isolated for more than 10 days.

## 3. Data Used

#### 3.1. Aspect

#### 3.2. Slope Gradient

#### 3.3. Altitude

#### 3.4. Curvature (Maximum and Profile)

#### 3.5. Topographic Wetness Index (TWI)

#### 3.6. Topographic Positioning Index (TPI)

#### 3.7. Distance from Fault

#### 3.8. Convexity

#### 3.9. Forest Factors (Forest Type, Forest Diameter, and Forest Density)

#### 3.10. Land Use/Land Cover (LULC)

#### 3.11. Lithology

#### 3.12. Soil

#### 3.13. Flow Accumulation

#### 3.14. Mid-Slope Position

## 4. Multicolinearity of Landslide Effective Factors

## 5. Modeling for Landslide Susceptibility Zonation

#### 5.1. Logistic Regression (LR)

#### 5.2. LogitBoost (LB)

#### 5.3. NaïveBayes (NB)

#### 5.4. Analysis of Spatial Relationship between Landslide Location and Effective Factors Based on Frequency Ratio (FR)

#### 5.5. Analysis of Independent Variable’s Importance

## 6. Results and Discussion

#### 6.1. Multicollinearity Analysis

#### 6.2. Spatial Relationship between Landslide Locations and Effective Factors

#### 6.3. Variable Contribution Analysis

#### 6.4. Landslide Susceptibility Models

#### 6.5. Accuracy Assessment and Their Comparison

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Landslide conditioning factor maps used in this study: (

**a**) aspect; (

**b**) slope; (

**c**) altitude; (

**d**) maximum curvature; (

**e**) profile curvature; (

**f**) topographic wetness index (TWI); (

**g**) topographic positioning index (TPI); (

**h**) distance from fault; (

**i**) convexity; (

**j**) forest types; (

**k**) forest diameter; (

**l**) forest density; (

**m**) land use and land cover (LULC); (

**n**) lithology; (

**o**) soil types; (

**p**) flow accumulation; and (

**q**) mid slope position.

**Figure 8.**Receiver operating characteristic (ROC) curve of the landslide susceptibility maps using validation data set.

Data | Sources | Scale/Resolution |
---|---|---|

Digital elevation model | National Geographic Information Institute (NGII) | 1:5000 |

Satellite image | Daum map | 0.5 × 0.5 m |

Soil map | National Academy of Agricultural Science (NAAS) | 1:5000 |

Lithology map | Korean Institute of Geoscience and Mineral Resources (KIGAM) | 1:25,000 |

Fault line | Korean Institute of Geoscience and Mineral Resources (KIGAM) | 1:25,000 |

Code | Formation | Lithology | Geological Age |
---|---|---|---|

Qa | Alluvium | Quaternary | |

PCEbgn | Banded gneiss | Quartzite and hornblende | Precambrian |

Null | - | - | - |

Jpbgr | Porphyritic biotite granite | Porphyritic biotite granite | Jurassic |

Jjgr | Jumunjin granite | Jumunjin granite | Jurassic |

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

Tolerance | VIF | |

Aspect | 0.962 | 1.039 |

Convexity | 0.409 | 2.445 |

Altitude | 0.280 | 3.572 |

Distance from fault | 0.482 | 2.076 |

Flow accumulation | 0.736 | 1.358 |

Forest density | 0.420 | 2.382 |

Forest diameter | 0.293 | 3.417 |

Forest type | 0.539 | 1.857 |

Land use/land cover (LU/LC) | 0.817 | 1.224 |

Lithology | 0.479 | 2.089 |

Maximum curvature | 0.369 | 2.706 |

Mid slope position | 0.621 | 1.612 |

Profile curvature | 0.479 | 2.089 |

Slope | 0.239 | 4.182 |

Soil types | 0.567 | 1.764 |

TPI | 0.323 | 3.096 |

TWI | 0.259 | 3.862 |

**Table 4.**Spatial relationship between each effective factor and gully erosion locations using frequency ratio (FR) model.

Factor | Class or Type | Landslide | %Landslide | Domain | %Domain | FR |
---|---|---|---|---|---|---|

Aspect | F | 34 | 12.41 | 261,192 | 10.87 | 1.14 |

N | 39 | 14.23 | 252,216 | 10.50 | 1.36 | |

NE | 39 | 14.23 | 284,988 | 11.86 | 1.20 | |

E | 19 | 6.93 | 264,632 | 11.02 | 0.63 | |

SE | 13 | 4.74 | 272,460 | 11.34 | 0.42 | |

S | 23 | 8.39 | 269,289 | 11.21 | 0.75 | |

SW | 21 | 7.66 | 265,477 | 11.05 | 0.69 | |

W | 48 | 17.52 | 260,769 | 10.86 | 1.61 | |

Slope angle | 1 | 5 | 1.82 | 478,026 | 19.90 | 0.09 |

2 | 22 | 8.03 | 478,284 | 19.91 | 0.40 | |

3 | 40 | 14.60 | 492,094 | 20.48 | 0.71 | |

4 | 77 | 28.10 | 475,589 | 19.80 | 1.42 | |

5 | 130 | 47.45 | 478,282 | 19.91 | 2.38 | |

Surface area | 1 | 2 | 0.73 | 308,094 | 12.83 | 0.06 |

2 | 46 | 16.79 | 897,345 | 37.35 | 0.45 | |

3 | 48 | 17.52 | 468,400 | 19.50 | 0.90 | |

4 | 86 | 31.39 | 396,462 | 16.50 | 1.90 | |

5 | 92 | 33.58 | 331,974 | 13.82 | 2.43 | |

Maximum curvature | concave | 51 | 18.61 | 726,645 | 30.25 | 0.62 |

flat | 89 | 32.48 | 889,637 | 37.03 | 0.88 | |

convex | 134 | 48.91 | 785,993 | 32.72 | 1.49 | |

Profile curvature | concave | 88 | 32.12 | 736,127 | 30.64 | 1.05 |

flat | 44 | 16.06 | 751,387 | 31.28 | 0.51 | |

convex | 142 | 51.82 | 914,761 | 38.08 | 1.36 | |

TWI | 1 | 123 | 44.89 | 457,389 | 19.04 | 2.36 |

2 | 66 | 24.09 | 508,138 | 21.15 | 1.14 | |

3 | 52 | 18.98 | 503,008 | 20.94 | 0.91 | |

4 | 32 | 11.68 | 482,196 | 20.07 | 0.58 | |

5 | 1 | 0.36 | 451,544 | 18.80 | 0.02 | |

TPI | 1 | 34 | 12.41 | 459,382 | 19.12 | 0.65 |

2 | 41 | 14.96 | 459,818 | 19.14 | 0.78 | |

3 | 28 | 10.22 | 482,381 | 20.08 | 0.51 | |

4 | 83 | 30.29 | 503,713 | 20.97 | 1.44 | |

5 | 88 | 32.12 | 496,981 | 20.69 | 1.55 | |

Distance of Fault (m) | 1 | 9 | 3.28 | 470,732 | 19.60 | 0.17 |

2 | 83 | 30.29 | 476,084 | 19.82 | 1.53 | |

3 | 114 | 41.61 | 481,862 | 20.06 | 2.07 | |

4 | 39 | 14.23 | 485,262 | 20.20 | 0.70 | |

5 | 29 | 10.58 | 488,335 | 20.33 | 0.52 | |

Convexity | 1 | 2 | 0.73 | 472,798 | 19.68 | 0.04 |

2 | 30 | 10.95 | 462,403 | 19.25 | 0.57 | |

3 | 56 | 20.44 | 473,777 | 19.72 | 1.04 | |

4 | 83 | 30.29 | 501,251 | 20.87 | 1.45 | |

Forest type | PK | 18 | 6.57 | 109,781 | 4.57 | 1.44 |

D | 204 | 74.45 | 1,121,961 | 46.70 | 1.59 | |

R | 0 | 0.00 | 7279 | 0.30 | 0.00 | |

L | 0 | 0.00 | 35,535 | 1.48 | 0.00 | |

PL | 15 | 5.47 | 17,874 | 0.74 | 7.36 | |

99 | 2 | 0.73 | 684,994 | 28.51 | 0.03 | |

PH | 0 | 0.00 | 5390 | 0.22 | 0.00 | |

PD | 3 | 1.09 | 5038 | 0.21 | 5.22 | |

M | 32 | 11.68 | 235,676 | 9.81 | 1.19 | |

H | 0 | 0.00 | 178,747 | 7.44 | 0.00 | |

Forest density | 0 | 23 | 8.39 | 838,044 | 34.89 | 0.24 |

C | 245 | 89.42 | 1,488,114 | 61.95 | 1.44 | |

B | 6 | 2.19 | 49,407 | 2.06 | 1.06 | |

A | 0 | 0.00 | 26,710 | 1.11 | 0.00 | |

Forest diameter | 0 | 7 | 1.08 | 727,808 | 30.30 | 0.04 |

1 | 63 | 9.71 | 110,236 | 4.59 | 2.12 | |

2 | 440 | 67.80 | 1,126,519 | 46.89 | 1.45 | |

3 | 139 | 21.42 | 437,712 | 18.22 | 1.18 | |

Land cover | 100 | 0 | 0.00 | 155,472 | 6.47 | 0.00 |

200 | 1 | 0.36 | 402,248 | 16.74 | 0.02 | |

300 | 229 | 83.58 | 1,626,301 | 67.70 | 1.23 | |

400 | 44 | 16.06 | 125,540 | 5.23 | 3.07 | |

500 | 0 | 0.00 | 5324 | 0.22 | 0.00 | |

600 | 0 | 0.00 | 42,429 | 1.77 | 0.00 | |

700 | 0 | 0.00 | 44,961 | 1.87 | 0.00 | |

Geology | Biotite porphyry | 0 | 0.00 | 21,258 | 0.88 | 0.00 |

Jumunjin granite | 107 | 39.05 | 1,319,764 | 54.94 | 0.71 | |

Alluvium | 0 | 0.00 | 205,364 | 8.55 | 0.00 | |

Banded gneiss | 0 | 0.00 | 131,399 | 5.47 | 0.00 | |

Biotite granite | 167 | 60.95 | 695,831 | 28.97 | 2.10 | |

Noname | 0 | 0.00 | 28,659 | 1.19 | 0.00 | |

Soil | SmF2 | 163 | 59.49 | 615,013 | 25.60 | 2.32 |

SgF2 | 31 | 11.31 | 221,518 | 9.22 | 1.23 | |

SgE2 | 58 | 21.17 | 575,574 | 23.96 | 0.88 | |

ScC | 0 | 0.00 | 49,114 | 2.04 | 0.00 | |

MuD | 1 | 0.36 | 14,197 | 0.59 | 0.62 | |

SlC | 1 | 0.36 | 14,200 | 0.59 | 0.62 | |

MuC | 6 | 2.19 | 34,360 | 1.43 | 1.53 | |

OsF | 0 | 0.00 | 75,573 | 3.15 | 0.00 | |

RC | 1 | 0.36 | 49,918 | 2.08 | 0.18 | |

SmF3 | 3 | 1.09 | 6315 | 0.26 | 4.17 | |

SlB | 0 | 0.00 | 1023 | 0.04 | 0.00 | |

SgD2 | 1 | 0.36 | 129,163 | 5.38 | 0.07 | |

OdF | 0 | 0.00 | 91 | 0.00 | 0.00 | |

W | 0 | 0.00 | 16,686 | 0.69 | 0.00 | |

BRS | 0 | 0.00 | 9403 | 0.39 | 0.00 | |

VcB | 0 | 0.00 | 26,155 | 1.09 | 0.00 | |

YaD2 | 1 | 0.36 | 54,643 | 2.27 | 0.16 | |

YaE2 | 0 | 0.00 | 22,274 | 0.93 | 0.00 | |

NkB | 0 | 0.00 | 9570 | 0.40 | 0.00 | |

ScB | 0 | 0.00 | 16,263 | 0.68 | 0.00 | |

Ki | 0 | 0.00 | 35,943 | 1.50 | 0.00 | |

YeB | 2 | 0.73 | 78,790 | 3.28 | 0.22 | |

YeC | 1 | 0.36 | 37,489 | 1.56 | 0.23 | |

SAC | 0 | 0.00 | 54,763 | 2.28 | 0.00 | |

SAB | 0 | 0.00 | 23,182 | 0.97 | 0.00 | |

BG | 0 | 0.00 | 24,473 | 1.02 | 0.00 | |

Jd | 0 | 0.00 | 13,064 | 0.54 | 0.00 | |

YdB | 0 | 0.00 | 2227 | 0.09 | 0.00 | |

Yf | 0 | 0.00 | 8782 | 0.37 | 0.00 | |

Ym | 0 | 0.00 | 6584 | 0.27 | 0.00 | |

Hh | 0 | 0.00 | 14,957 | 0.62 | 0.00 | |

Kw | 0 | 0.00 | 6810 | 0.28 | 0.00 | |

YaC2 | 0 | 0.00 | 2310 | 0.10 | 0.00 | |

HuB | 0 | 0.00 | 42,744 | 1.78 | 0.00 | |

SgE3 | 5 | 1.82 | 64,836 | 2.70 | 0.68 | |

JoB | 0 | 0.00 | 1068 | 0.04 | 0.00 | |

Ng | 0 | 0.00 | 3245 | 0.14 | 0.00 | |

JiB | 0 | 0.00 | 8284 | 0.34 | 0.00 | |

BqB | 0 | 0.00 | 8370 | 0.35 | 0.00 | |

Dq | 0 | 0.00 | 4438 | 0.18 | 0.00 | |

Gq | 0 | 0.00 | 5517 | 0.23 | 0.00 | |

HT | 0 | 0.00 | 9881 | 0.41 | 0.00 | |

SoD2 | 0 | 0.00 | 1299 | 0.05 | 0.00 | |

Gt | 0 | 0.00 | 1458 | 0.06 | 0.00 | |

Hl | 0 | 0.00 | 708 | 0.03 | 0.00 | |

Flow accumulation | 1 | 53 | 19.34 | 483,922 | 20.14 | 0.96 |

2 | 92 | 33.58 | 549,109 | 22.86 | 1.47 | |

3 | 63 | 22.99 | 517,391 | 21.54 | 1.07 | |

4 | 38 | 13.87 | 432,821 | 18.02 | 0.77 | |

5 | 28 | 10.22 | 419,032 | 17.44 | 0.59 | |

Mid slope position | 1 | 81 | 29.56 | 474,165 | 19.74 | 1.50 |

2 | 69 | 25.18 | 476,110 | 19.82 | 1.27 | |

3 | 57 | 20.80 | 465,025 | 19.36 | 1.07 | |

4 | 27 | 9.85 | 498,482 | 20.75 | 0.47 | |

5 | 40 | 14.60 | 488,493 | 20.33 | 0.72 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Pourghasemi, H.R.; Gayen, A.; Park, S.; Lee, C.-W.; Lee, S. Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms. *Sustainability* **2018**, *10*, 3697.
https://doi.org/10.3390/su10103697

**AMA Style**

Pourghasemi HR, Gayen A, Park S, Lee C-W, Lee S. Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms. *Sustainability*. 2018; 10(10):3697.
https://doi.org/10.3390/su10103697

**Chicago/Turabian Style**

Pourghasemi, Hamid Reza, Amiya Gayen, Sungjae Park, Chang-Wook Lee, and Saro Lee. 2018. "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms" *Sustainability* 10, no. 10: 3697.
https://doi.org/10.3390/su10103697