Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China

Jiaqi Zhao; Qiang Zhang; Danzhou Wang; Wenhuan Wu; Ruyue Yuan

doi:10.1007/s13753-022-00401-w

Volume 13 Issue 2

Jul. 2022

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Article Navigation > International Journal of Disaster Risk Science > 2022 > 13(2): 305-316

Jiaqi Zhao, Qiang Zhang, Danzhou Wang, Wenhuan Wu, Ruyue Yuan. Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China[J]. International Journal of Disaster Risk Science, 2022, 13(2): 305-316. doi: 10.1007/s13753-022-00401-w

Citation:

Jiaqi Zhao, Qiang Zhang, Danzhou Wang, Wenhuan Wu, Ruyue Yuan. Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China[J]. International Journal of Disaster Risk Science, 2022, 13(2): 305-316. doi: 10.1007/s13753-022-00401-w

Citation:

Jiaqi Zhao, Qiang Zhang, Danzhou Wang, Wenhuan Wu, Ruyue Yuan. Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China[J]. International Journal of Disaster Risk Science, 2022, 13(2): 305-316. doi: 10.1007/s13753-022-00401-w

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Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China

doi: 10.1007/s13753-022-00401-w

Jiaqi Zhao^1,2,3,
Qiang Zhang^{1,2,3
,
,},
Danzhou Wang^1,2,3,
Wenhuan Wu^1,2,3,
Ruyue Yuan^1,2,3

1 Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing, 100875, China;
2 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China;
3 Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing, 100875, China

Funds:

D Program, Grant No. 2019YFA0606900 and by the Scientific Investigation of the Qinghai-Tibet Plateau, Grant No. 2019QZKK0900. We would like to extend our cordial gratitude to the editors, Dr. Ying Li, Dr. Juan Du and the anonymous reviewers for their pertinent and professional comments and suggestions that were greatly helpful for further quality improvement of this manuscript.

This research has been financially supported by the China National Key R&

Available Online: 2022-07-06

Abstract

Abstract

The Hengduan Mountains Region (HMR) is one of the areas that experience the most frequent geological hazards in China. However, few reports are available that address the geological hazard susceptibility of the region. This study developed six machine learning models to assess the geological hazard susceptibility. The results show that areas with medium and high susceptibility to geological hazards as a whole account for almost 21% of the total area, while both are 18% when it comes to the single hazard of landslide and rockfall respectively. Medium and high geological hazard susceptibility is found in three parts of the HMR with different characteristics: (1) the central and southern parts, where the population of the region concentrates; (2) the northern part, where higher geological hazard susceptibility is found along the mountain ranges; and (3) the junction of Tibet, Yunnan, and Sichuan in the eastern part, which is prone to larger-scale geological hazards. Of all the potential influencing factors, topographic features and climatic variables act as the major driving factors behind geological hazards and elevation, slope, and precipitation are crucial indicators for geological hazard susceptibility assessment. This study developed the geological hazard susceptibility maps of the HMR and provided information for the multi-hazard risk assessment and management of the region.
- Geological hazards,
- Landslides,
- Machine learning techniques,
- Rockfalls,
- Susceptibility evaluation

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References

Bednarik, M., I. Yilmaz, and M. Marschalko. 2012. Landslide hazard and risk assessment:A case study from the Hlohovec-Sered' landslide area in south-west Slovakia. Natural Hazards 64(1):547-575.

Catani, F., D. Lagomarsino, S. Segoni, and V. Tofani. 2013. Landslide susceptibility estimation by random forests technique:Sensitivity and scaling issues. Natural Hazards and Earth System Sciences 13(11):2815-2831.

Chen, Y., W. Xie, and X. Xu. 2019. Changes of population, built-up land, and cropland exposure to natural hazards in china from 1995 to 2015. International Journal of Disaster Risk Science 10(4):557-572.

Chen, J., S. Zhao, W. Liao, and W. Yuan. 2009. Research on natural disaster risk assessment model based on Support Vector Machine and it's application. In Neural information processing. ICONIP 2009, ed. C.S. Leung, M. Lee, and J.H. Chan, 762-769. Heidelberg:Springer.

China, People's Republic of. Ministry of Land and Resources. 2014. Specification of comprehensive survey for landslide, collapse and debris flow (1:50000). Beijing:Standards Press of China. http://www.nssi.org.cn/nssi/front/87543223.html.

CIGEM (China Institute of Geological Environmental Monitoring). 2011. Rockfall and landslide disaster map of China. Beijing:SinoMaps Press.

Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T. Hess, J.C. Gibson, and J.J. Lawler. 2007. Random forests for classification in ecology. Ecology 88(11):2783-2792.

Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, S. Dhakal, and P. Paudyal. 2008. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102(3-4):496-510.

Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, T. Masuda, and K. Nishino. 2008. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology 54(2):314-324.

Erener, A., and H.B.S. Düzgün. 2013. A regional scale quantitative risk assessment for landslides:Case of Kumluca watershed in Bartin, Turkey. Landslides 10(1):55-73.

Fell, R., J. Corominas, C. Bonnard, L. Cascini, E. Leroi, W.Z. Savage on behalf of the JTC-1 Joint Technical Committee on Landslides and Engineered Slopes. 2008. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102:99-111.

Floris, M., M. Iafelice, C. Squarzoni, L. Zorzi, A. De Agostini, and R. Genevois. 2011. Using online databases for landslide susceptibility assessment:An example from the Veneto Region (northeastern Italy). Natural Hazards and Earth System Sciences 11(7):1915-1925.

Gorsevski, P.V., P.E. Gessler, R.B. Foltz, and W.J. Elliot. 2006. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS 10(3):395-415.

Hadji, R., A.E. Boumazbeur, Y. Limani, M.A. Baghem, A.M. Chouabi, and A. Demdoum. 2013. Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling:A case study of Souk Ahras region, NE Algeria. Quaternary International 302:224-237.

Hadmoko, D.S., F. Lavigne, J. Sartohadi, P. Hadi, and Winaryo. 2010. Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Natural Hazards 54(3):623-642.

Hou, J., J. Lv, X. Chen, and S. Yu. 2016. China's regional social vulnerability to geological disasters:Evaluation and spatial characteristics analysis. Natural Hazards 84(1):97-111.

Huang, R. 2009. Some catastrophic landslides since the twentieth century in the southwest of China. Landslides 6(1):69-81.

Huang, J., B. Di, J. Bian, J. Zuo, and X. Hu. 2014. Analysis on the spatial distribution characteristics and human driving forces of mountain disasters in Liangshan Prefecture. Soil And Water Conservation Research 21(6):278-283.

Huang, P., L. Peng, and H. Pan. 2020. Linking the Random Forests Model and GIS to assess geo-hazards risk:A case study in Shifang County, China. IEEE Access 8:28033-28042.

Koks, E.E., J. Rozenberg, C. Zorn, M. Tariverdi, M. Vousdoukas, S.A. Fraser, J.W. Hall, and S. Hallegatte. 2019. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications 10(1):Article 2677.

Lee, S., and K. Min. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology 40(9):1095-1113.

Lee, S., J.H. Ryu, and I.S. Kim. 2007. Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models:Case study of Youngin, Korea. Landslides 4(4):327-338.

Li, Z., Y. He, C. Wang, X. Wang, H. Xin, W. Zhang, and W. Cao. 2011. Spatial and temporal trends of temperature and precipitation during 1960-2008 at the Hengduan mountains. Quaternary International 236(1):127-142.

Lin, J., W. Chen, X. Qi, and H. Hou. 2021. Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment. Journal of Cleaner Production 309(9):Article 127077.

Lin, Q., W. Ying, T. Liu, Y. Zhu, and Q. Sui. 2017. The vulnerability of people to landslides:A case study on the relationship between the casualties and volume of landslides in China. International Journal of Environmental Research and Public Health 14(2):Article 212.

Liu, C., W. Li, H. Wu, P. Lu, K. Sang, W. Sun, W. Chen, Y. Hong, and R. Li. 2013. Susceptibility evaluation and mapping of China's landslides based on multi-source data. Natural Hazards 69(3):1477-1495.

Mandal, S., and R. Maiti. 2015. Application of analytical hierarchy process (AHP) and frequency ratio (FR) model in assessing landslide susceptibility and risk. In Semi-quantitative approaches for landslide assessment and prediction, ed. S. Mandal, and R. Maiti, 191-226. Heidelberg:Springer.

Niu, C., Q. Wang, J. Chen, W. Zhang, L. Xu, and K. Wang. 2015. Hazard assessment of debris flows in the reservoir region of Wudongde hydropower station in China. Sustainability 7(11):15099-15118.

Peduzzi, P., H. Dao, C. Herold, and F. Mouton. 2009. Assessing global exposure and vulnerability towards natural hazards:The disaster risk index. Natural Hazards & Earth System Sciences 9(4):1149-1159.

Pourghasemi, H.R., A.G. Jirandeh, B. Pradhan, C. Xu, and C. Gokceoglu. 2013. Landslide susceptibility mapping using support vector machine and GIS. Journal of Earth System Science 61(15):349-369.

Pourghasemi, H.R., N. Kariminejad, M. Amiri, M. Edalat, M. Zarafshar, T. Blaschke, and A. Cerda. 2020. Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Scientific Reports 10:Article 3203.

Pourghasemi, H.R., B. Pradhan, and C. Gokceoglu. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards 63(2):965-996.

Pouyan, S., H.R. Pourghasemi, M. Bordbar, S. Rahmanian, and J.J. Clague. 2021. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports 11(1):Article 14889.

Qiu, J. 2014. Landslide risks rise up agenda. Nature 511(7509):272-273.

Saha, S., A. Arabameri, A. Saha, T. Blaschke, and S.S. Band. 2020. Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Science of the Total Environment 764:Article 142928.

Samia, J., A. Temme, A. Bregt, J. Wallinga, and F. Ardizzone. 2020. Dynamic path dependent landslide susceptibility modelling. Natural Hazards and Earth System Sciences 20(1):271-285.

Shahabi, H., and M. Hashim. 2015. Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Scientific Reports 5:Article 9899.

Sidle, R.C., and H. Ochiai. 2006. Landslides:Processes, prediction and land use. Washington, DC:American Geophysical Union.

Sidle, R.C., T. Furuichi, and Y. Kono. 2011. Unprecedented rates of landslide and surface erosion along a newly constructed road in Yunnan, China. Natural Hazards 57:313-326.

Sidle, R.C., and A.D. Ziegler. 2012. The dilemma of mountain roads. Nature Geoscience 5(7):437-438.

Strehmel, A., S. Schnbrodt-Stitt, G. Buzzo, C. Dumperth, and T. Scholten. 2015. Assessment of geo-hazards in a rapidly changing landscape:The Three Gorges Reservoir region in China. Environmental Earth Sciences 74(6):4939-4960.

Süzen, M.L., and V. Doyuran. 2004. A comparison of the GIS based landslide susceptibility assessment methods:Multivariate versus bivariate. Environmental Geology 45(5):665-679.

Trigila, A., C. Iadanza, C. Esposito, and G. Scarascia-Mugnozza. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119-136.

Wang, Y., Y. Guo, and W. Li. 2005. Prediction model based on improved BP neural network and its application. Computer Measurement and Control 13(1):39-42.

Xu, C., F. Dai, X. Xu, and Y.H. Lee. 2012. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145-146:70-80.

Xu, R., X. Li, K. Hu, and Y. Nie. 2019. A dynamic hazard assessment for mountain hazards in Hengduan mountain area. Journal of Catastrophology 34(3):196-201, 208.

Yao, X., L.G. Tham, and F.C. Dai. 2008. Landslide susceptibility mapping based on support vector machine:A case study on natural slopes of Hong Kong, China. Geomorphology 101:572-582.

Yilmaz, I. 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison:A case study from Kat landslides(Tokat-Turkey). Computers & Geosciences 35(6):1125-1138.

Ying, Z., W. Hong-Wei, W. Yu-Bing, and G. Peng-Cheng. 2017. A novel optimization algorithm for BP neural network based on RS-MEA. In Proceedings of 2017 2nd International Conference on Image, Vision and Computing (ICIVC 2017), 2-4 June 2017, Chengdu, China. Piscataway, NJ:Institute of Electrical and Electronics Engineers.

Zhang, G., Z. Nan, X. Wu, H. Ji, and S. Zhao. 2019. The role of winter warming in permafrost change over the Qinghai-Tibet Plateau. Geophysical Research Letters 46(20):11261-11269.

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