Local-scale landslide susceptibility mapping using the B-GeoSVC model
- 177 Downloads
Local-scale landslide susceptibility mapping (LSM) provides detailed information for decision making and planning. Most published landslide susceptibility maps lack reliable information at the local scale due to the spatial heterogeneity being ignored. To enrich the local-scale information of LSM, multiple information fusion methods for the local spatial heterogeneity and regional trends of control factors are needed. However, no information fusion method has been proposed for LSM yet. In this paper, we developed a new integrated statistical method, named B-GeoSVC, under the hierarchical Bayesian framework for LSM. Specifically, this model applied the GeoDetector method to fit the regional trends of control factors and employed spatially varying coefficients (SVC) model to fit the local spatial heterogeneity of each control factor. Then, the regional trends and local spatial heterogeneity information were fused within the hierarchical Bayesian framework. The B-GeoSVC model was verified using data from the Duwen basin of China, which was in the central region affected by the MS 8.0 Wenchuan earthquake that occurred on May 12, 2008. Under a cross-validation experiment, the prediction accuracy rate of the B-GeoSVC model was 86.09%, and the area under the curve was 0.93, which suggested that the B-GeoSVC model was able to achieve relatively accurate local-scale LSM and provide richer local information than traditional regional scale LSM. More importantly, not only the B-GeoSVC model could be employed as a general solution to fuse both regional and local-scale information for landslide mapping, but also offer new insights into the broader earth science and spatial statistics.
KeywordsLandslide susceptibility mapping Spatial heterogeneity Regional and local information fusion GeoDetector SVC Hierarchical Bayesian method
The authors are grateful to Jinfeng Wang (LREIS) for his valuable suggestions to improve our work and supporting freeware GeoDetector. We appreciate Yan Zhen and the colleagues in the Spatial Information Technology and Big Data Mining Research Center in Southwest Petroleum University for their assistance in this study. We would also like to thank the editors and five anonymous reviewers for their constructive comments and valuable suggestions in improving this manuscript.
The work was jointly supported by the National Natural Science Foundation of China (no. 41701448), a grant from State Key Laboratory of Resources and Environmental Information System (no. 201811), and the Young Scholars Development Fund of Southwest Petroleum University (no. 201699010064), the Open Fund of the State Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resource (no. KLGSIT2016-03), the Technology Project of the Sichuan Bureau of Surveying, Mapping and Geoinformation (no. J2017ZC05), and the Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau (no. NC17SY4016, 18SXHZ0025).
- Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28:281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x CrossRefGoogle Scholar
- Ciurean RL, Hussin H, Van Westen CJ, Jaboyedoff M, Nicolet P, Chen L, Frigerio S, Glade T (2017) Multi-scale debris flow vulnerability assessment and direct loss estimation of buildings in the eastern Italian Alps. Nat Hazards 85:929–957. https://doi.org/10.1007/s11069-016-2612-6 CrossRefGoogle Scholar
- Cui P, Wei F, He S (2008) Mountain disasters induced by the earthquake of May 12 in Wenchuan and the disasters mitigation. J Mt Sci 26:280–282 (in Chinese)Google Scholar
- Dai Z, Wei Y, Lv T, Luo J, Yao W (2016) Deformation influence factors of a landslide in Three Gorges Reservoir area based on grey correlation analysis. The Chinese Journal of Geological Hazard and Control 27:32-37. https://doi.org/10.16031/j.cnki.issn.1003-8035.2016.01.06
- Gan JJ, Huang RQ, Fan CR, Qian-Yin LI, Xiao-Hua YE (2011) A study of the slope failure along the Dujiangyan to Wenchuan highway after the Wenchuan earthquake. Hydrogeol Eng Geol 38:59–65 (in Chinese)Google Scholar
- Hong H, Ilia I, Tsangaratos P, Chen W, Xu C (2017) A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the wuyuan area, China. Geomorphology 290:1–16. https://doi.org/10.1016/j.geomorph.2017.04.002
- Hong H, Pradhan B, Bui DT, Xu C, Youssef AM, Chen W (2016) Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomat Nat Haz Risk 8:544–569. https://doi.org/10.1080/19475705.2016.1250112 CrossRefGoogle Scholar
- Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007 CrossRefGoogle Scholar
- Pollett WG, Gibbs P, Mclaughlin S, Eteuati J, Harold M, Marion K, Patel S, Jones I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6 CrossRefGoogle Scholar
- Song C, He Y, Bo Y, Wang J, Ren Z, Yang H (2018a) Risk assessment and mapping of hand, foot, and mouth disease at the county level in mainland China using spatiotemporal zero-inflated bayesian hierarchical models. Int J Environ Res Public Health 15:1476. https://doi.org/10.3390/ijerph15071476 CrossRefGoogle Scholar
- Song C, Shi X, Bo YC, Wang JF, Wang Y, Huang DC (2019) Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian spatiotemporally varying coefficients (STVC) model in Sichuan, China. Sci Total Environ 648:550–560. https://doi.org/10.1016/j.scitotenv.2018.08.114 CrossRefGoogle Scholar
- Wang JF, Hu Y (2012) Software, data and modelling news. In: Environmental health risk detection with geogdetector, vol 33. Elsevier Science Publishers B. V, pp 114–115. https://doi.org/10.1016/j.envsoft.2012.01.015
- Wen C, Xiao H, Zeng J (2015) Evaluation of landslide stability based on catastrophe progression method. J Nat Disast Sci 24:68–73 (in Chinese)Google Scholar
- Yang JT, Song C, Yang Y, Xu CD, Guo F, Xie L (2019) New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: a case study of Duwen Highway Basin, Sichuan Province, China. Geomorphology 324:62–71. https://doi.org/10.1016/j.geomorph.2018.09.019 CrossRefGoogle Scholar
- Zhuang J, Peng C, Ge Y, Zhu Y, Liu Y, Pei L (2010) Risk assessment of collapses and landslides caused by 5.12 wenchuan earthquake—a case study of Dujiangyan-Wenchuan highway. Chin J Rock Mech Eng 29:3735–3742 (in Chinese)Google Scholar