# A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale

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## Abstract

**:**

## 1. Introduction

## 2. Overview of the Methodology

## 3. Mega-Zonation of the Earthquake-Induced Liquefaction Risk in Continental Europe

#### 3.1. Mapping the Probability of Liquefaction by Applying A European Prediction Model

- The weighted-mean shear-wave velocity in the top 30 m (V
_{S30}), which was adopted as a proxy of soil stiffness since soft sandy soils are more susceptible to liquefaction (they are looser). The US Geological Survey (https://earthquake.usgs.gov/data/vs30/) provided the global topographic-slope based V_{S30}map and such a map was adopted for Europe; - The weighted-magnitude peak ground acceleration (PGAm), which was computed as

_{S30}map. In doing so, only 1D lithostratigraphic amplification was explicitly considered. MWF stands for Magnitude-Weighting Factor, which is defined by the following equation:

_{S30}). In the logistic regression, the probability of liquefaction is therefore calculated using the following equation:

_{k}are the explanatory variables and γ

_{k}the coefficients of the regression. By integrating the optimal geospatial predictors into Equation (4), the latter can be rewritten as follows:

#### 3.2. Exposure Model for Europe

^{2}unit, which is the most common format to express the population density. The data were grouped into five classes of exposure, as done for the probability of liquefaction in Section 3.1. In particular, the following classes for the population density (Pd) were adopted:

- very low: Pd < 400 inhab./km
^{2}; - low: 400 ≤ Pd 800 inhab./km
^{2}; - medium: 800 ≤ Pd < 2000 inhab./km
^{2}; - high: 2000 ≤ Pd < 5000 inhab./km
^{2}; - very high: Pd ≥ 5000 inhab./km
^{2}.

#### 3.3. Assessment of the Liquefaction Risk at the European Scale by Using the AHP Technique

_{max}− n)⁄(n − 1)

_{max}is the maximum eigenvalue of the judgement matrix and n is the rank of the matrix. CI is then compared with that of a Random Matrix (RI). The corresponding ratio, i.e., CI/RI, is termed the Consistency Ratio (CR). Saaty [8] suggested that the upper threshold value of CR should be 0.1.

#### 3.4. European Charts for Earthquake-Induced Liquefaction Risk

## 4. Discussion and Concluding Remarks

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- National Academies of Sciences, Engineering, and Medicine. State of the Art and Practice in the Assessment of Earthquake-Induced Soil Liquefaction and Its Consequences; The National Academies Press: Washington, DC, USA, 2016. [Google Scholar] [CrossRef]
- Zhu, J.; Daley, D.; Baise, L.; Thompson, E.; Wald, D.; Knudsen, K. A Geospatial Liquefaction Model for Rapid Response and Loss Estimation. Earthq. Spectra
**2015**, 31, 1813–1837. [Google Scholar] [CrossRef] - Matsuoka, M.; Wakamatsu, K.; Hashimoto, M.; Senna, S.; Midorikawa, S. Evaluation of liquefaction potential for large areas based on geomorphologic classification. Earthq. Spectra
**2015**, 31, 2375–2395. [Google Scholar] [CrossRef] - Zhu, J.; Baise, L.; Thompson, E. An Updated Geospatial Liquefaction Model for Global Application. Bull. Seismol. Soc. Am.
**2017**, 107, 1365–1385. [Google Scholar] [CrossRef] - Rashidian, V.; Baise, L.G. Regional efficacy of a global geospatial liquefaction model. Eng. Geol.
**2020**, 272. [Google Scholar] [CrossRef] - Yilmaz, C.; Silva, V.; Weatherill, G. Probabilistic framework for regional loss assessment due to earthquake-induced liquefaction including epistemic uncertainty. Soil Dyn. Earthq. Eng.
**2020**, in press. [Google Scholar] [CrossRef] - Bozzoni, F.; Bonì, R.; Conca, D.; Lai, C.G.; Zuccolo, E.; Meisina, C. Megazonation of earthquake-induced soil liquefaction hazard in continental Europe. Bull. Earthq. Eng.
**2020**, in press. [Google Scholar] [CrossRef] - Saaty, T.L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980. [Google Scholar]
- Karimzadeh, S.; Miyajima, M.; Hassanzadeh, R.; Amiraslanzadeh, R.; Kamel, B. A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz. Soil Dyn. Earthq. Eng.
**2014**, 66, 263–280. [Google Scholar] [CrossRef] - Panahi, M.; Rezaie, F.; Meshkani, S. Seismic vulnerability assessment of school buildings in Tehran city based on AHP and GIS. Nat. Hazards Earth Syst. Sci.
**2015**, 15, 461–474. [Google Scholar] [CrossRef][Green Version] - Moustafa, S.S.R. Application of the Analytic Hierarchy Process for Evaluating Geo-Hazards in the Greater Cairo Area, Egypt. Electron. J. Geotech. Eng.
**2015**, 20, 1921–1938. Available online: http://www.ejge.com/2015/Ppr2015.0207sb.pdf (accessed on 18 December 2020). - Lai, C.G.; Conca, D.; Bozzoni, F.; Bonì, R.; Meisina, C. Earthquake-induced soil liquefaction risk: Macrozonation of the European territory taking into account exposure. In Proceedings of the IABSE Symposium 2019 Guimarães. Towards a Resilient Built Environment—Risk and Asset Management, Guimarães, Portugal, 27–29 March 2019. [Google Scholar]
- UNESCO. Report of Consultative Meeting of Experts on the Statistical Study of Natural Hazards and Their Consequences; United Nations Educational Scientific and Cultural Organizations Document SC/WS/500; UNESCO: Paris, France, 1972. [Google Scholar]
- Lai, C.G.; Meisina, C.; Bozzoni, F.; Conca, D.; Bonì, R. Report to Describe the Adopted Procedure for the Development of the European Liquefaction Potential Map; Deliverable D2.6. V 1.0. Liquefact Project, H2020-DRA-2015, GA No. 700748; 2019; Available online: http://www.liquefact.eu/wp-content/uploads/2020/03/D2.6.pdf (accessed on 18 December 2020).
- Meisina, C.; Bonì, R.; Bozzoni, F.; Conca, D.; Perotti, C.; Persichillo, P.; Lai, C.G. Assessment of the soil liquefaction susceptibility across Europe using Analytic Hierarchy Process (AHP). Eng. Geol.
**2020**, submitted. [Google Scholar] - Cornell, C.A.; Luco, N. Ground motion intensity measures for structural performance assessment at near-fault sites. In Proceedings of the Proceedings U.S.-Japan Joint Workshop and Third Grantees Meeting, Seattle, WA, USA, 15–16 August 2001. [Google Scholar]
- Luco, N.; Cornell, C.A. Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthq. Spectra
**2007**, 23, 357–392. [Google Scholar] [CrossRef][Green Version] - Padgett, J.E.; Nielson, B.G.; DesRoches, R. Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios. Earthq. Eng. Struct. Dyn.
**2008**, 37, 711–725. [Google Scholar] [CrossRef] - Wang, X.; Shafieezadeh, A.; Ye, A. Optimal intensity measures for probabilistic seismic demand modeling of extended pile-shaft-supported bridges in liquefied and laterally spreading ground. Bullettin Earthq. Eng.
**2018**, 16, 229–257. [Google Scholar] [CrossRef] - Jarvis, A.; Reuter, H.I.; Nelson, A.; Guevara, E. Hole-Filled SRTM for the Globe. Version 4. 2008. Available online: http://srtm.csi.cgiar.org (accessed on 18 December 2020).
- Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull.
**1979**, 24, 43–69. [Google Scholar] [CrossRef][Green Version] - Eurocode 8. Design of Structures for Earthquake Resistance, Part 1: General Rules, Seismic Actions and Rules for Buildings; Pr-EN1998-1; European Committee for Standardization (CEN): Brussels, Belgium, 2004.
- Chen, R.; Harmsen, S. Probabilistic Ground Motion Calculations and Implementation of PGA Scaling by Magnitude for Assessing Liquefaction Potential, Technical Document 2012-1, Seismic Hazard Zonation Program. 2012.
- Youd, T.L.; Idriss, I.M.; Andrus, R.D.; Arango, I.; Castro, G.; Christian, J.T.; Dobry, R.; Finn, W.D.L.; Harder, L.F.; Hynes, M.E.; et al. Liquefaction resistance of soils: Summary report from the 1996. NCEER and 1998 NCEER/NSF workshops on the evaluation of liquefaction resistance of soils. J. Geotech. Geoenviron. Eng.
**2001**, 127, 817–833. [Google Scholar] [CrossRef][Green Version] - Yen, S.J.; Lee, Y.S. Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset. In Intelligent Control and Automation. Lecture Notes in Control and Information Sciences; Huang, D.S., Li, K., Irwin, G.W., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; Volume 344. [Google Scholar] [CrossRef]
- Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res.
**2002**, 16, 321–357. [Google Scholar] [CrossRef] - He, H.; Bai, Y.; Garcia, E.A.; Li, S. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. In Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1–8 October 2008. [Google Scholar]
- Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett.
**2005**, 27, 861–874. [Google Scholar] [CrossRef] - Sousa, L.; Silva, V.; Bazzurro, P. Using Open-Access Data in the Development of Exposure Data Sets of Industrial Buildings for Earthquake Risk Modeling. Earthq. Spectra
**2017**, 33, 63–84. [Google Scholar] [CrossRef]

**Figure 1.**Flowchart of the geospatial approach developed in this study for mapping the liquefaction risk at a continental scale.

**Figure 2.**Maps showing the liquefaction potential for the European territory referred to the return period of 475 years [7]. The results are expressed (

**a**) as a binary outcome, i.e., liquefaction or no liquefaction, and (

**b**) according to a chromatic scale based on five different classes of the probability of liquefaction. Locations of liquefaction occurrences (black dots) associated with a return period of about 475 years are superimposed to both charts. The grey areas are a priori excluded because of either the geological-based or the seismic-hazard based filters herein applied (the greyscale is based on Digital Elevation Model, DEM).

**Figure 3.**Exposure model for Europe adopted in this study by combining open-access data on population density and land cover in Europe, used as proxies for urbanized areas and strategic infrastructures, respectively.

**Figure 4.**European liquefaction risk maps were calculated in this study for the return periods of 475 (

**a**), 975 (

**b**), and 2475 (

**c**) years. The grey areas are a priori excluded because of the geological-based and seismic-hazard based filters applied (the greyscale is based on the DEM).

**Table 1.**Application of Analytical Hierarchy Process (AHP) method: relative importance for comparison between different alternatives [8].

Weight/Rank | Relative Importance |
---|---|

1 | equal |

3 | moderately dominant |

5 | strongly dominant |

7 | very strongly dominant |

9 | extremely dominant |

2, 4, 6, 8 | intermediate values |

Reciprocals | for inverse judgements |

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**MDPI and ACS Style**

Bozzoni, F.; Bonì, R.; Conca, D.; Meisina, C.; Lai, C.G.; Zuccolo, E. A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale. *Geosciences* **2021**, *11*, 32.
https://doi.org/10.3390/geosciences11010032

**AMA Style**

Bozzoni F, Bonì R, Conca D, Meisina C, Lai CG, Zuccolo E. A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale. *Geosciences*. 2021; 11(1):32.
https://doi.org/10.3390/geosciences11010032

**Chicago/Turabian Style**

Bozzoni, Francesca, Roberta Bonì, Daniele Conca, Claudia Meisina, Carlo G. Lai, and Elisa Zuccolo. 2021. "A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale" *Geosciences* 11, no. 1: 32.
https://doi.org/10.3390/geosciences11010032