# Study on the Risk Assessment Method of Rainfall Landslide

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

#### 2.2. Landslide Data

## 3. Regional Security Risk Assessment

#### 3.1. Analysis of Influencing Factors

#### 3.1.1. Topography

#### 3.1.2. Rock–Soil Mass Types

#### 3.1.3. Geological Structure

#### 3.1.4. Rainfall

#### 3.2. Risk Analysis

#### 3.2.1. Determination of Slope Unit

#### 3.2.2. The Method of Hazard Assessment

_{L}= P(N

_{L}) × P(S)

#### 3.2.3. The Method of Vulnerability Assessment

#### 3.2.4. Risk Assessment and Characteristics Analysis

## 4. Individual Landslide Risk Assessment

#### 4.1. Landslide Features

^{4}m

^{2}. The vegetation on the landslide slope is more developed and mainly covered by shrubs and citrus trees. The material composition of the landslide is mainly residual slope sedimentary silty clay, siltstone, and silty mudstone. The slip mass is mainly strongly weathered silty mudstone. The altitude of the rock is 330°∠28°. The joint fissures are extremely developed and open, and the average occurrence is about 162°∠65°. It is speculated that the sliding surface is rock mass level. The seasonal variation of groundwater level and water volume in the landslide area is strong, and the water volume varies greatly with atmospheric precipitation.

#### 4.2. Probability Analysis of Extreme Rainfall

_{0}, α, and β are the location parameter, shape, and scale of Pearson III distribution, respectively, which can be expressed as $\alpha =\frac{4}{{C}_{s}^{2}},\text{}\beta =\frac{2}{E\left(x\right){C}_{V}{C}_{S}}$, ${a}_{0}=E\left(x\right)\left(1-\frac{2{C}_{V}}{{C}_{S}}\right)$; E (x) is the mathematical expectation; C

_{V}and C

_{s}are the off-potential coefficient and skewness coefficient. The density function can be determined after a

_{0}, α, and β are determined.

#### 4.3. Risk Assessment of Landslide

## 5. Conclusions

- (1)
- According to a large amount of statistical data analysis, the main influencing factors of landslide disasters in Mayang County are topography, geological lithology, geological structure, and rainfall. Controlled by formation lithology in space, most of them are developed in the distribution area of red clastic rocks; controlled by rainfall distribution in time, they mainly occur from April to July and have a positive correlation with rainfall.
- (2)
- In this paper, taking slope units as the basic evaluation unit, the risk calculation formula proposed by Varnes was used to evaluate the risk of slopes and the vulnerability of the disaster-bearing bodies. Comprehensively considering the population and economic risks, the geological disaster risk assessment of regions in Mayang County was completed. From the perspective of spatial distribution, there were 45 slope units in high-risk areas, which were scattered throughout the key areas and more distributed in Shiyangshao Township–Yanmen Town–Changtan Village Yanmen Town; there were 195 medium-risk slope units, which were mainly distributed in Shiyangshao Township–Yanmen Town; there were 119 low-risk slope units.
- (3)
- In this paper, taking the Shiyantan Landslide in Hunan Province as an example, the landslide disaster risk assessment was carried out. Landslides are generally stable in natural and half-saturated conditions, but slopes are highly likely to cause damages in case of extreme rainfall (e.g., five consecutive five days of rainfall in 50 years). In this case, the Shiyantan Landslide has low economic risk and high life risk, so it is necessary to formulate relevant measures to alleviate the risk.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Zhen, Z.; Shihai, L.; Li, M. Probability analysis of relationship between landslide and rainfall in Chongqing Area. Chin. J. Rock Mech. Eng.
**2005**, 24, 3185–3191. [Google Scholar] - Zhang, M.; Hu, R.; Tan, R. State-of-the-art study on landslides due to rainfall and the prospect. Geotech. Investig. Surv.
**2009**, 37, 11–17. [Google Scholar] - Tang, H.M.; Wei, L.; Tang, Y.H.; Gao, Y. Correlation analysis and prediction model for rainfallinduced landslide in Chongqing area. Chin. J. Geol. Hazard Control
**2013**, 24, 16–22. [Google Scholar] - Caine, N. The Rainall intensity: Duration control of shallow landslides and debris flows. Geogr. Annaler. Ser. A Phys. Geogr.
**1980**, 62, 23–27. [Google Scholar] - Chen, C.; Saito, H.; Oguchi, T. Rainfall intensity-duration conditions for mass movements in Taiwan. Prog. Earth Planet. Sci.
**2015**, 2, 1–13. [Google Scholar] [CrossRef] - Aleotti, P. A warning system for rainfall-induced shallow failures. Eng. Geol.
**2004**, 73, 247–265. [Google Scholar] [CrossRef] - Zhou, H.; Sun, S.; Shang, W.; Huang, Y. Analysis of the soil slope stability influenced by rainfall pattern and intensity. Sci. Technol. Eng.
**2012**, 12, 2602–2606. [Google Scholar] - Zhao, L.; Li, D.; Tan, H.; Cheng, X.; Zuo, S. Characteristics of failure area and failure mechanism of a bedding rockslide in Libo County, Guizhou, China. Landslides
**2019**, 16, 1367–1374. [Google Scholar] [CrossRef] - Yuming, W.U.; Hengxing, L.A.; Xing, G.A.; Langping, L.I. Rainfall threshold of storm-induced landslides in typhoon areas: A case study of Fujian Province. J. Eng. Geol.
**2014**, 22, 255–262. [Google Scholar] - Ma, T.; Li, C.; Sun, L.; Li, W.; He, C. Rainfall intensity-duration thresholds for landslides in Zhejiang region, China. Chin. J. Geol. Hazard Control
**2011**, 22, 20–25. [Google Scholar] - Xing, Q.; Qiang, X.; Liang, S.; Xu, S.; Zhang, T. Research overview on early warning of precipitation-induced loess landslides. Geol. Sci. Technol. Inform.
**2014**, 33, 219–225. [Google Scholar] - Ke-qiang, H.; Dong, G.; Peng, Z.; Lu, G.; Guo-dong, Z. The vertical displacement direction rate of landslides induced by rainfall and its early warning destabilized criterion. Rock Soil Mech.
**2017**, 38, 3649–3659. [Google Scholar] - Zhang, J.; Lu, T.; Xue, J.F.; Zheng, W.T. Modified Green-Ampt model for analyzing rainfall infiltration in slopes. Rock Soil Mech.
**2016**, 37, 2451–2457. [Google Scholar] - Tang, Y.M.; Zhang, M.S.; Xue, Q.; Bi, J.B. Landslide monito-ring and early-warning: An overview. Geol. Rev.
**2012**, 58, 533–541. [Google Scholar] - Yin, K.L.; Chen, L.X.; Zhang, G.R. Regional landslide hazard warning and risk assessment. Earth Sci. Front.
**2007**, 14, 85–97. [Google Scholar] [CrossRef] - Nguyen, B.Q.V.; Kim, Y.T. Regional-scale landslide risk assessment on Mt. Umyeon using risk index estimation. Landslides
**2021**, 18, 2547–2564. [Google Scholar] [CrossRef] - Cross, M. Landslide susceptibility mapping using the Matrix Assessment Approach: A Derbyshire case study. Geol. Soc. Lond. Eng. Geol. Spec. Publ.
**1998**, 15, 247–261. [Google Scholar] [CrossRef] - Dai, F.C.; Lee, C.F.; Ngai, Y.Y. Landslide risk assessment and management: An overview. Eng. Geol.
**2002**, 64, 65–87. [Google Scholar] [CrossRef] - Du, J. Risk Assessment of Individual Landslide; China University of Geosciences: Wuhan, China, 2012. [Google Scholar]
- Fell, R.; Corominas, J.; Bonnard, C.; Cascini, L.; Leroi, E.; Savage, W.Z. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng. Geol.
**2007**, 102, 85–98. [Google Scholar] [CrossRef] - Tyagi, A.; Tiwari, R.K.; James, N. GIS-based landslide hazard zonation and risk studies using MCDM. In Local Site Effects and Ground Failures; Springer: Singapore, 2021; pp. 251–266. [Google Scholar]
- Xiang, X.Q.; Huang, R.Q. Risk assessment and risk management for slope geohazards. J. Geol. Hazards Environ. Preserv.
**2000**, 11, 38–41. [Google Scholar] - Ma, Y.S.; Zhang, Y.C.; Zhang, C.S.; Wang, J.S. Theory and approaches to the risk evaluation of geological hazards. J. Geomech.
**2004**, 10, 7–18. [Google Scholar] - Tang, C.; Zhu, J. Approach for urban debris flow risk assessment. Adv. Water Sci.
**2006**, 17, 383–388. [Google Scholar] - Wu, S.R.; Shi, J.S.; Zhang, C.S.; Wang, T. Preliminary discussion on technical guideline for geohazard risk assessment. Geol. Bull. China
**2009**, 28, 995–1005. [Google Scholar] - Ruilin, H.; Linfeng, F.; Shanshan, W.; Lichao, W.; Xueliang, W. Theory and method for landslide risk assessment-current status and future development. J. Eng. Geol.
**2013**, 21, 76–84. [Google Scholar] - Chen, L.X.; Yin, K.L.; Wang, Y. Discussion on risk prediction for single landslide. J. Nat. Disasters
**2008**, 17, 65–70. [Google Scholar] - Wang, N.; Peng, K.; Li, Q.; Zhao, X.; Li, Y.; He, J. Quantitative evaluation of geological disaster liability based on RS & GIS analysis: A case study of Wufeng County, Hubei Province. Earth Sci. Front.
**2012**, 19, 221–229. [Google Scholar] - Yang, Z.H.; Zhang, Y.S.; Guo, C.B. Landslide hazard rapid assessment in the Ms8.1 nepal earthquake-impacted area, based on Newmark model. J. Geomech.
**2017**, 23, 115–124. [Google Scholar] - Li, X.; Xue, G.C.; Liu, C.Z. Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island. J. Geomech.
**2022**, 28, 294–305. [Google Scholar] - Glade, T. Vulnerability assessment in landslide risk analysis. Beitrag Zur Erdsystemforschung
**2003**, 134, 123–146. [Google Scholar] - Yin, K.L.; Zhang, G.R. Risk zonation of geo-hazards and its comprehensive control. Saf. Environ. Eng.
**2003**, 10, 32–35. [Google Scholar] - Uzielli, M.; Nadim, F.; Lacasse, S.; Kaynia, A.M. A conceptual framework for quantitative estimation of physical vulnerability to landslides. Eng. Geol.
**2008**, 102, 251–256. [Google Scholar] [CrossRef] - Hungr, O.; McDougall, S. Two numerical models for landslide dynamic analysis. Comput. Geo-Sci.
**2009**, 35, 978–992. [Google Scholar] [CrossRef] - Anderson, M.G.; Holcombe, E.; Esquivel, M.; Toro, J.; Ghesquiere, F. The efficacy of a programme of landslide risk reduction in areas of unplanned housing in the Eastern Caribbean. Environ. Manag.
**2010**, 45, 807–821. [Google Scholar] [CrossRef] - Papathoma-Köhle, M.; Kappes, M.; Keiler, M.; Glade, T. Physical vulnerability assessment for alpine hazards: State og the art and future needs. Nat. Hazards
**2011**, 58, 645–680. [Google Scholar] [CrossRef] - Fotopoulou, S.; Pitilakis, K. Vulnerability assessment of reinforced concrete buildings subjected to seismically triggered slow-moving earth slides. Landslides
**2013**, 10, 563–582. [Google Scholar] [CrossRef] - Guanyu, L.I.; Peng, L.I.; Min, G.U.O. Application of cluster analysis method in geological hazard risk assessment: A case study of Hancheng City. Sci. Technol. Eng.
**2021**, 21, 10629–10638. [Google Scholar] - Zhang, Y.; Wu, J.; Xu, B. Human health risk assessment of groundwater nitrogen pollution in Jinghui canal irrigation area of the loess region, northwest China. Environ. Earth Sci.
**2018**, 77, 273. [Google Scholar] [CrossRef] - Varnes, D.J.; IAEG. Commission on Landslide and Other Mass Movement on Slopes; Landslide hazard zonation: A review of principles and practice; The UNESCO Press: Paris, France, 1984; 63p. [Google Scholar]
- Chung, M.C.; Tan, C.H.; Chen, C.H. Local rainfall thresholds for forecasting landslide occurrence: Taipingshan landslide triggered by Typhoon Saola. Landslides
**2017**, 14, 19–33. [Google Scholar] [CrossRef] - Benson, M.A. Uniform Flood-Frequency Estimating Methods for Federal Agencies. Water Resour. Res.
**1968**, 4, 891–908. [Google Scholar] [CrossRef] - Li, P.; He, X.; Li, Y.; Xiang, G. Occurrence and Health Implication of Fluoride in Groundwater of Loess Aquifer in the Chinese Loess Plateau: A Case Study of Tongchuan, Northwest China. Expo Health
**2019**, 11, 95–107. [Google Scholar] [CrossRef] - Li, P.; He, X.; Guo, W. Spatial groundwater quality and potential health risks due to nitrate ingestion through drinking water: A case study in Yan’an City on the Loess Plateau of northwest China. Hum. Ecol. Risk Assess.
**2019**, 25, 11–31. [Google Scholar] [CrossRef]

**Figure 7.**The distribution of landslide occurrence and rainfall over the study period 2005 to 2017, (

**a**) for yearly distribution and (

**b**) for monthly distribution.

**Figure 15.**Photos of the Shiyantan landslide ((

**a**) damaged house at the leading edge of the landslide (

**b**) tensile crack).

Degree | Reappearing Period of 10 Years | Reappearing Period of 20 Years | ||
---|---|---|---|---|

The Number of Units | Percentage | The Number of Units | Percentage | |

High-risk region | 46 | 12.8% | 74 | 20.6% |

Medium-risk region | 146 | 40.7% | 198 | 55.2% |

Low-risk region | 167 | 46.5% | 87 | 24.2% |

Vs | Affected Area | Slip Body |
---|---|---|

Soil-wood | 0.9 | 1.0 |

Brick-wood | 0.7 | 1.0 |

Brick-concrete | 0.5 | 1.0 |

Railway | 0.7 | 1.0 |

Motorway | 0.6 | 1.0 |

Highway | 0.3 | 1.0 |

Transmission line | 0.7 | 1.0 |

Village road | 0.3 | 1.0 |

Farmland | 0.3 | 1.0 |

Orchard | 0.3 | 1.0 |

Wasteland | 0.7 | 1.0 |

Minerals | 0.6 | 1.0 |

Vulnerability | Soil-Wood | Brick-Wood | Brick-Concrete |
---|---|---|---|

0.1 | 0.00 | 0.00 | 0.00 |

0.3 | 0.01 | 0.01 | 0.01 |

0.5 | 0.02 | 0.02 | 0.02 |

0.8 | 0.08 | 0.13 | 0.13 |

1 | 0.24 | 0.45 | 0.45 |

Different Rainfall Durations/d | E(x)/mm | C_{v} | C_{s} |
---|---|---|---|

1 | 99.069 | 0.28622 | 1.15347 |

3 | 133.115 | 0.31363 | 1.35802 |

5 | 146.385 | 0.25625 | 0.63550 |

7 | 169.762 | 0.23527 | 1.32693 |

Return Periods | IDe (1 Day) | IDe (3 Days) | IDe (5 Days) | IDe (7 Days) |
---|---|---|---|---|

10 | 3.34 | 4.61 | 4.79 | 5.45 |

50 | 4.22 | 5.99 | 5.74 | 6.76 |

100 | 4.58 | 6.55 | 6.11 | 7.29 |

Conditions | Stability Coefficient | Failure Probability (%) |
---|---|---|

1 | 1.293 | 7.06% |

2 | 1.048 | 35.42% |

3 | 0.883 | 59.18% |

Structural Typology | S_{str} | S_{mai} | S_{ser} | S_{dir} | I_{f-dep} | I_{fai-s} | V |
---|---|---|---|---|---|---|---|

Brick-concrete | 0.5 | 0.2 | 0.1 | 0.7 | 1.0 | 1.0 | 0.89 |

Half-timbered | 0.7 | 0.35 | 0.7 | 0.7 | 1.0 | 1.0 | 0.98 |

Type | Number | Unit Price (yuan) | Price (10^{4} yuan) | Total (10^{4} yuan) |
---|---|---|---|---|

Farmland | 6000 m^{2} | 30 | 18 | 667.42 |

Half-timbered building | 672 m^{2} | 600 | 40.32 | |

Brick-concrete structural building | 2995 m^{2} | 1800 | 539.1 | |

Village road | 300 m | 1000 | 30 | |

Citrus orchard | 8000 m^{2} | 50 | 40 |

Most Dangerous Condition | Deadweight + Complete Saturation (50 Years) |
---|---|

Annual instability probability (%) | 1.183 |

Total economic value (10^{4} yuan) | 667.42 |

Number of people | 103 |

Societal economic risk(10^{4} yuan) | 4.69 |

Individual economic risk(10^{4} yuan) | 4.55 × 10^{−2} |

Total life risk | 0.13 |

Individual life risk | 1.19 × 10^{−3} |

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**MDPI and ACS Style**

Sui, H.; Su, T.; Hu, R.; Wang, D.; Zheng, Z. Study on the Risk Assessment Method of Rainfall Landslide. *Water* **2022**, *14*, 3678.
https://doi.org/10.3390/w14223678

**AMA Style**

Sui H, Su T, Hu R, Wang D, Zheng Z. Study on the Risk Assessment Method of Rainfall Landslide. *Water*. 2022; 14(22):3678.
https://doi.org/10.3390/w14223678

**Chicago/Turabian Style**

Sui, Haoyue, Tianming Su, Ruilin Hu, Dong Wang, and Zhengwei Zheng. 2022. "Study on the Risk Assessment Method of Rainfall Landslide" *Water* 14, no. 22: 3678.
https://doi.org/10.3390/w14223678