# Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

#### 2.1.1. Study Area and Landslide Inventory Information

^{2}with a length of 71.8 km and a width of 53.7 km. The total population is about 3.33 × 10

^{5}. Shicheng County belongs to the subtropical monsoon humid climate zone and has abundant sunshine, four distinctive seasons, and rich rainfall. Its average annual precipitation was about 1748.6 mm between 1970 and 2015, and the total precipitation of the main flood season (April–June) accounts for 50.1% of the total annual precipitation. Both precipitation and temperature are non-uniformly distributed in Shicheng County due to the complex terrain characteristics and the relationship between land and sea locations.

^{2}). The Qin River flows through the whole area from northeast to southwest and finally flows into the Ganjiang River. The groundwater type of Shicheng County is mainly shallow groundwater of shallow depth, good recharge conditions, rapid regeneration speed, and easy extraction. Furthermore, Shicheng County is in a mountain basin surrounded by the Wuyi Mountains. These mountains are mainly composed of pre-Devonian metamorphic rocks, Devonian quartz sandstone, sandy conglomerate, and sandy shale. In general, Shicheng County is in a typical southeast hilly region, with many mountains in the northeast area, rolling hills in the southwest area, and flat terrain in the central area.

^{6}m

^{2}, and the area of these landslides ranges from about 1.0 × 10

^{3}to 1.6 × 10

^{4}m

^{2}. Furthermore, the main direct trigger factors of these landslides are seasonal heavy rainfall and frequent unreasonable human engineering activities, such as slope toe cutting and road excavation.

#### 2.1.2. Landslide-Related Predisposing Factors

- (1)
- Topography factors in Shicheng County

- (2)
- Hydrological, lithological, and land cover factors

#### 2.1.3. FR and Correlation Analysis of Predisposing Factors

#### 2.2. Methods

#### 2.2.1. Multilayer Perceptron

#### 2.2.2. Theory of PSO-MLP Model

## 3. Results

#### 3.1. Training and Testing Variables of the Four Models

#### 3.2. PSO-MLP Model for LSP

#### 3.3. MLP-Only Model for LSP

#### 3.4. BPNN Model

#### 3.5. IV Model for LSP

## 4. Discussion

#### 4.1. Frequency Ratio Accuracy Analysis

#### 4.2. ROC Accuracies of These Models

#### 4.3. PSO-MLP Model-Building Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Topography factors in Shicheng County: (

**a**) DEM, (

**b**) Slope, (

**c**) Aspect, (

**d**) Relief amplitude, (

**e**) Plan curvature, (

**f**) Profile curvature.

**Figure 3.**Hydrological, lithological, and land cover factors in Shicheng County, (

**a**) Distance to river, (

**b**) TWI, (

**c**) MNDWI, (

**d**) Rock types, (

**e**) NDBI, (

**f**) NDVI, (

**g**) Total surface radiation, (

**h**) Population density index.

**Figure 6.**Landslide susceptibility maps respectively produced by PSO-MLP (

**a**), MLP-only (

**b**), BPNN (

**c**), and IV (

**d**) models.

Factors | Data Type | Value | Grids in Domain | Grid Proportion (%) | Landslide Grid Number | Grid Proportion (%) | Frequency Ratio |
---|---|---|---|---|---|---|---|

DEM (m) | Continuous | 0–280.9 | 450,325 | 25.6 | 833 | 30.7 | 1.200 |

280.9–360.9 | 544,197 | 31.0 | 943 | 34.8 | 1.124 | ||

360.9–454.2 | 308,108 | 17.5 | 307 | 11.3 | 0.646 | ||

454.2–560.9 | 181,087 | 10.3 | 341 | 12.6 | 1.222 | ||

560.9–676.4 | 127,080 | 7.2 | 199 | 7.3 | 1.016 | ||

676.4–805.3 | 86,871 | 4.9 | 82 | 3.0 | 0.612 | ||

805.3–969.7 | 41,033 | 2.3 | 4 | 0.1 | 0.063 | ||

969.7–1320.1 | 18,636 | 1.1 | 0 | 0 | 0 | ||

Slope (°) | Continuous | 0–3.9 | 348,797 | 19.8 | 184 | 6.8 | 0.342 |

3.9–7.5 | 406,976 | 23.2 | 737 | 27.2 | 1.175 | ||

7.5–11.2 | 358,445 | 20.4 | 865 | 31.9 | 1.566 | ||

11.2–14.9 | 258,803 | 14.7 | 516 | 19.0 | 1.293 | ||

14.9–19.1 | 184,620 | 10.5 | 273 | 10.1 | 0.959 | ||

19.1–23.8 | 114,448 | 6.5 | 113 | 4.2 | 0.641 | ||

23.8–29.8 | 61,718 | 3.5 | 18 | 0.7 | 0.189 | ||

29.8–52.8 | 23,530 | 1.3 | 3 | 0.1 | 0.083 | ||

Aspect | Continuous | –1 | 359 | 0.02 | 0 | 0 | 0 |

0–22.5, 337.5–360 | 204,837 | 11.7 | 258 | 9.5 | 0.817 | ||

22.5–67.5 | 176,594 | 10.0 | 233 | 8.6 | 0.856 | ||

67.5–112.5 | 212,635 | 12.1 | 439 | 16.2 | 1.339 | ||

112.5–157.5 | 230,991 | 13.1 | 379 | 14.0 | 1.064 | ||

157.5–202.5 | 225,837 | 12.9 | 276 | 10.2 | 0.793 | ||

202.5–247.5 | 211,352 | 12.0 | 263 | 9.7 | 0.807 | ||

247.5–292.5 | 239,169 | 13.6 | 464 | 17.1 | 1.259 | ||

Relief amplitude | Continuous | 0–22.4 | 335,770 | 19.1 | 393 | 14.5 | 0.759 |

22.4–38.3 | 420,761 | 23.9 | 924 | 34.1 | 1.425 | ||

38. 3–54.2 | 349,311 | 19.9 | 673 | 24.8 | 1.250 | ||

54.2–71.5 | 270,334 | 15.4 | 457 | 16.9 | 1.097 | ||

71.5–91.0 | 182,134 | 10.4 | 188 | 6.9 | 0.670 | ||

91.0–114.9 | 111,395 | 6.3 | 73 | 2.7 | 0.425 | ||

114.9–146.7 | 60,885 | 3.5 | 1 | 0.04 | 0.011 | ||

146.7–185 | 26,747 | 1.5 | 0 | 0 | 0 | ||

Plan curvature | Continuous | 0–9.909 | 330,252 | 18.8 | 714 | 26.4 | 1.403 |

9.909–18.54 | 351,942 | 20.0 | 748 | 27.6 | 1.379 | ||

18.54–27.49 | 269,313 | 15.3 | 468 | 17.3 | 1.127 | ||

27.49–37.08 | 206,319 | 11.7 | 292 | 10.8 | 0.918 | ||

37.08–47.628 | 167,821 | 9.5 | 198 | 7.3 | 0.765 | ||

47.628–58.497 | 133,993 | 7.6 | 84 | 3.1 | 0.407 | ||

58.497–70.324 | 126,193 | 7.2 | 74 | 2.7 | 0.380 | ||

70.324–81.5 | 171,504 | 9.8 | 131 | 4.8 | 0.496 | ||

Profile curvature | Continuous | 0–1.694 | 475,975 | 27.1 | 716 | 26.4 | 0.976 |

1.694–3.267 | 455,721 | .25.9 | 799 | 29.5 | 1.137 | ||

3.267–4.961 | 349,124 | 19.9 | 508 | 18.8 | 0.944 | ||

4.961–6.776 | 225,132 | 12.8 | 347 | 12.8 | 0.999 | ||

6.776–8.832 | 135,555 | 7.7 | 185 | 6.8 | 0.885 | ||

8.832–11.373 | 73,762 | 4.2 | 104 | 3.8 | 0.915 | ||

11.373–15.003 | 33,044 | 1.9 | 45 | 1.7 | 0.883 | ||

15.003–30.8 | 9024 | 0.5 | 5 | 0.2 | 0.359 | ||

Distance to river (m) | Discrete | 0–250 | 319,909 | 18.2 | 1237 | 45.7 | 2.508 |

250–500 | 291,189 | 16.6 | 447 | 16.5 | 0.996 | ||

500–750 | 262,670 | 14.9 | 234 | 8.6 | 0.578 | ||

750–3000 | 883,569 | 50.3 | 791 | 29.2 | 0.581 | ||

TWI | Continuous | 0–6.165 | 327,344 | 18.6 | 430 | 15.9 | 0.852 |

6.165–7.256 | 488,501 | 27.8 | 800 | 29.5 | 1.062 | ||

7.256–8.346 | 401,144 | 22.8 | 718 | 26.5 | 1.161 | ||

8.346–9.601 | 259,094 | .14.7 | 476 | 17.6 | 1.192 | ||

9.601–11.128 | 138,598 | 7.9 | 164 | 6.1 | 0.768 | ||

11.128–13.037 | 78,193 | 4.4 | 60 | 2.2 | 0.498 | ||

13.037–15.6 | 42,782 | 2.4 | 42 | 1.6 | 0.637 | ||

15.6–18 | 21,681 | 1.2 | 19 | 0.7 | 0.569 | ||

MNDWI | Continuous | 0–0.145 | 94,750 | 5.4 | 121 | 4.5 | 0.828 |

0.145–0.278 | 187,275 | 10.7 | 324 | 12.0 | 1.122 | ||

0.278–0.392 | 258,082 | 14.7 | 492 | 18.2 | 1.237 | ||

0.392–0.502 | 296,664 | 16.9 | 616 | 22.7 | 1.347 | ||

0.502–0.612 | 297,008 | 16.9 | 541 | 20.0 | 1.182 | ||

0.612–0.729 | 273,311 | 15.5 | 348 | 12.8 | 0.826 | ||

0.729–0.859 | 211,515 | 12.0 | 183 | 6.8 | 0.561 | ||

0.859–1 | 138,732 | 07.9 | 84 | 3.1 | 0.393 | ||

Rock types | Discrete | Metamorphic rock | 919,176 | 52.3 | 1450 | 53.5 | 1.023 |

Carbonate rock | 500,159 | 28.5 | 639 | 23.6 | 0.829 | ||

Clastic rock | 337,500 | 19.2 | 620 | 22.9 | 1.192 | ||

Water | 502 | 0.03 | 0 | 0 | 0 | ||

NDBI | Continuous | 0–0.231 | 220,622 | 12.6 | 143 | 5.3 | 0.421 |

0.231–0.302 | 407,692 | 23.2 | 324 | 12.0 | 0.516 | ||

0.302–0.373 | 385,678 | 21.9 | 502 | 18.5 | 0.844 | ||

0.373–0.451 | 283,274 | 16.1 | 575 | 21.2 | 1.317 | ||

0.451–0.545 | 211,706 | 12.0 | 561 | 20.7 | 1.719 | ||

0.545–0.659 | 142,090 | 8.1 | 349 | 12.9 | 1.593 | ||

0.659–0.812 | 77,712 | 4.4 | 200 | 7.4 | 1.670 | ||

0.812–1 | 28,563 | 1.6 | 55 | 2.0 | 1.249 | ||

NDVI | Continuous | 0–0.205 | 21,416 | 1.2 | 18 | 0.7 | 0.545 |

0.205–0.363 | 48,274 | 2.7 | 133 | 4.9 | 1.787 | ||

0.363–0.46 | 140,192 | 8.0 | 353 | 13.0 | 1.633 | ||

0.46–0.53 | 277,504 | 15.8 | 584 | 21.6 | 1.365 | ||

0.53–0.593 | 412,360 | 23.5 | 663 | 24.5 | 1.043 | ||

0.593–0.651 | 382,238 | 21.8 | 460 | 17.0 | 0.781 | ||

0.651–0.721 | 322,632 | 18.4 | 384 | 14.2 | 0.772 | ||

0.721–1 | 152,721 | 8.7 | 114 | 4.2 | 0.484 | ||

Total surface radiation | Continuous | 0–0.459 | 10,052 | 0.6 | 10 | 0.4 | 0.645 |

0.459–0.592 | 27,582 | 1.6 | 56 | 2.1 | 1.317 | ||

0.592–0.678 | 61,099 | 3.5 | 80 | 3.0 | 0.849 | ||

0.678–0.753 | 111,044 | 6.3 | 172 | 6.3 | 1.005 | ||

0.753–0.816 | 170,418 | 9.7 | 297 | 11.0 | 1.131 | ||

0.816–0.875 | 262,906 | 15.0 | 400 | 14.8 | 0.987 | ||

0.875–0.929 | 409,406 | 23.3 | 550 | 20.3 | 0.872 | ||

0.929–1 | 704,830 | 40.1 | 1144 | 42.2 | 1.053 | ||

Population density index | Continuous | 0–0.678 | 20,286 | 1.2 | 7 | 0.3 | 0.224 |

0.678–0.733 | 78,566 | 4.5 | 80 | 3.0 | 0.661 | ||

0.733–0.776 | 116,553 | 6.6 | 102 | 3.8 | 0.568 | ||

0.776–0.820 | 200,139 | 11.4 | 266 | 9.8 | 0.862 | ||

0.820–0.863 | 257,308 | 14.6 | 300 | 11.1 | 0.756 | ||

0.863–0.906 | 311,063 | 17.7 | 543 | 20.0 | 1.132 | ||

0.906–0.949 | 380,425 | 21.6 | 657 | 24.3 | 1.120 | ||

0.949–1 | 392,997 | 22.4 | 754 | 27.8 | 1.245 |

Models | Class | Total Grid Number | Proportion (%) | Landslide Grid Number | Proportion (%) | FR Values |
---|---|---|---|---|---|---|

PSO-MLP | Very low | 414,852 | 23.6 | 36 | 1.3 | 0.056 |

Low | 435,746 | 24.8 | 209 | 7.7 | 0.311 | |

Moderate | 451,412 | 25.7 | 477 | 17.6 | 0.685 | |

High | 275,386 | 15.7 | 726 | 26.8 | 1.710 | |

Very high | 179,941 | 10.2 | 1261 | 46.5 | 4.546 | |

MLP-only | Very low | 485,954 | 27.7 | 107 | 3.9 | 0.143 |

Low | 402,491 | 22.9 | 224 | 8.3 | 0.361 | |

Moderate | 398,323 | 22.7 | 452 | 16.7 | 0.736 | |

High | 296,913 | 16.9 | 763 | 28.2 | 1.667 | |

Very high | 173,656 | 9.9 | 1163 | 42.9 | 4.344 | |

BPNN | Very low | 451,506 | 25.7 | 72 | 2.7 | 0.103 |

Low | 393,108 | 22.4 | 198 | 7.3 | 0.327 | |

Moderate | 437,828 | 24.9 | 467 | 17.2 | 0.692 | |

High | 282,685 | 16.1 | 719 | 26.5 | 1.650 | |

Very high | 192,208 | 10.9 | 1253 | 46.3 | 4.229 | |

IV | Very low | 417,155 | 23.7 | 81 | 3.0 | 0.126 |

Low | 400,663 | 22.8 | 222 | 8.2 | 0.359 | |

Moderate | 468,615 | 26.7 | 549 | 20.3 | 0.760 | |

High | 302,688 | 17.2 | 772 | 28.5 | 1.655 | |

Very high | 168,214 | 9.6 | 1085 | 40.1 | 4.184 |

© 2019 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**

Li, D.; Huang, F.; Yan, L.; Cao, Z.; Chen, J.; Ye, Z.
Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models. *Appl. Sci.* **2019**, *9*, 3664.
https://doi.org/10.3390/app9183664

**AMA Style**

Li D, Huang F, Yan L, Cao Z, Chen J, Ye Z.
Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models. *Applied Sciences*. 2019; 9(18):3664.
https://doi.org/10.3390/app9183664

**Chicago/Turabian Style**

Li, Deying, Faming Huang, Liangxuan Yan, Zhongshan Cao, Jiawu Chen, and Zhou Ye.
2019. "Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models" *Applied Sciences* 9, no. 18: 3664.
https://doi.org/10.3390/app9183664