Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model

Bofan Yu; Jiaxing Yan; Yunan Li; Huaixue Xing

doi:10.1007/s13753-024-00578-2

Volume 15 Issue 4

Aug. 2024

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Article Navigation > International Journal of Disaster Risk Science > 2024 > 15(4): 640-656

Bofan Yu, Jiaxing Yan, Yunan Li, Huaixue Xing. Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model[J]. International Journal of Disaster Risk Science, 2024, 15(4): 640-656. doi: 10.1007/s13753-024-00578-2

Citation:

Bofan Yu, Jiaxing Yan, Yunan Li, Huaixue Xing. Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model[J]. International Journal of Disaster Risk Science, 2024, 15(4): 640-656. doi: 10.1007/s13753-024-00578-2

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Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model

doi: 10.1007/s13753-024-00578-2

1. The Institute of Geological Survey of China University of Geosciences (Wuhan), China University of Geosciences (Wuhan), Wuhan, 430074, China;
2. China Geological Survey, Nanjing Center, Nanjing, 210016, China

Funds:

The work described in this article was funded by the Laboratory of Geological Safety of Underground Space in Coastal Cities, Ministry of Natural Resources (Project No. BHKF2022Z02), and the China Geological Survey, Nanjing Center (Project No. DD20190281).

Accepted Date: 2024-08-12

Available Online: 2024-10-26

Publish Date: 2024-08-22

Abstract

Abstract

As the global push for sustainable urban development progresses, this study, set against the backdrop of Hangzhou City, one of China’s megacities, addressed the conflict between urban expansion and the occurrence of urban geological hazards. Focusing on the predominant geological hazards troubling Hangzhou—urban road collapse, land subsidence, and karst collapse—we introduced a Categorical Boosting-SHapley Additive exPlanations (CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting the selected typical urban hazards, with area under the curve (AUC) values reaching 0.92, 0.92, and 0.94, respectively, but also, through the incorporation of the explainable model SHAP, visually presents the prediction process, the interrelations between evaluation factors, and the weight of each factor. Additionally, the study undertook a multi-hazard evaluation, producing a susceptibility zoning map for multiple hazards, while performing tailored analysis by integrating economic and population density factors of Hangzhou. This research enables urban decision makers to transcend the “black box” limitations of machine learning, facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area. This approach holds the potential to offer valuable insights for the sustainable development of cities worldwide.
- Hangzhou city,
- Multi-hazard risk assessment,
- Machine learning,
- Machine learning interpretability,
- Socioeconomic analysis,
- Urban sustainability development

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References(64)

References

[1]	Alikaei, S., M. Rahmani, F. Jamalabadi, M.E. Akdogan, and S. Khoshnevis. 2023. Multi-hazard-based land use planning in isolated area; Learning from the experience of Pule-Khumri City, Afghanistan. Sustainable Cities and Society 99: Article 104873.
[2]	Al-Qubatee, W., F.A. Hasan, H. Ritzema, G. Nasher, and P. Hellegers. 2022. Natural and human-induced drivers of groundwater depletion in Wadi Zabid, Tihama coastal plain, Yemen. Journal of Environmental Planning and Management 65(14): 2609-2630.
[3]	Bagheri-Gavkosh, M., S.M. Hosseini, B. Ataie-Ashtiani, Y. Sohani, H. Ebrahimian, F. Morovat, and S. Ashrafi. 2021. Land subsidence: A global challenge. Science of the Total Environment 778: Article 146193.
[4]	Bishop, C.M. 2006. Pattern recognition and machine learning. New York: Springer.
[5]	Brownlee, J. 2020. Data preparation for machine learning: Data cleaning, feature selection, and data transforms in Python. https://github.com/aaaastark/Data-Scientist-Books/blob/main/Data%20Preparation%20for%20Machine%20Learning%20Data%20Cleaning%2C%20Feature%20Selection%2C%20and%20Data%20Transforms%20in%20Python%20by%20Jason%20Brownlee%20(z-lib.org).pdf. Accessed 15 Jun 2024.
[6]	Castelvecchi, D. 2016. Can we open the black box of AI?. Nature 538(7623): 21-23.
[7]	Chen, F., H. Lin, Y. Zhang, and Z. Lu. 2012. Ground subsidence geo-hazards induced by rapid urbanization: Implications from InSAR observation and geological analysis. Natural Hazards and Earth System Sciences 12(4): 935-942.
[8]	Chen, Y., J. Song, S. Zhong, Z. Liu, and W. Gao. 2022. Effect of destructive earthquake on the population-economy-space urbanization at county level-A case study on Dujiangyan County, China. Sustainable Cities and Society 76: Article 103345.
[9]	Costa, V.G., and C.E. Pedreira. 2023. Recent advances in decision trees: An updated survey. Artificial Intelligence Review 56(5): 4765-4800.
[10]	Cui, Z.-D., J.-Q. Yang, and L. Yuan. 2015. Land subsidence caused by the interaction of high-rise buildings in soft soil areas. Natural Hazards 79(2): 1199-1217.
[11]	De Waele, J., F. Gutiérrez, M. Parise, and L. Plan. 2011. Geomorphology and natural hazards in karst areas: A review. Geomorphology 134(1-2): 1-8.
[12]	Etinay, N., C. Egbu, and V. Murray. 2018. Building urban resilience for disaster risk management and disaster risk reduction. Procedia Engineering 212(6): 575-582.
[13]	Ganesh, B., S. Vincent, S. Pathan, and S.R. Garcia Benitez. 2023. Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution. Remote Sensing Applications: Society and Environment 29(19): Article 100905.
[14]	Godschalk, D.R. 2003. Urban hazard mitigation: Creating resilient cities. Natural Hazards Review 4(3): 136-143.
[15]	Gray, M.A. 1990. The United Nations Environment Programme: An assessment. Environmental Law 20(2): Article 291.
[16]	Gu, H., S. Du, B. Liao, J. Wen, C. Wang, R. Chen, and B. Chen. 2018. A hierarchical pattern of urban social vulnerability in Shanghai, China and its implications for risk management. Sustainable Cities and Society 41: 170-179.
[17]	Gutiérrez, F., M. Parise, J. De Waele, and H. Jourde. 2014. A review on natural and human-induced geohazards and impacts in karst. Earth-Science Reviews 138: 61-88.
[18]	Haghiri, M., N. Raeisi, R. Azizi, K. Shabani, and M. Ghadiri. 2024. Evaluation of karst aquifer development and karst water resource potential using fuzzy logic model (FAHP) and analysis hierarchy process (AHP): A case study, North of Iran. Carbonates and Evaporites 39(2). https://doi.org/10.1007/s13146-024-00925-w.
[19]	Hancock, J.T., and T.M. Khoshgoftaar. 2020. CatBoost for big data: An interdisciplinary review. Journal of Big Data 7(1): Article 94.
[20]	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.
[21]	Hu, R.L., Z.Q. Yue, L.C. Wang, and S.J. Wang. 2004. Review on current status and challenging issues of land subsidence in China. Engineering Geology 76(1-2): 65-77.
[22]	Ibrahim, A.A., R.L. Ridwan, M.M. Muhammed, R.O. Abdulaziz, and G.A. Saheed. 2020. Comparison of the CatBoost classifier with other machine learning methods. International Journal of Advanced Computer Science and Applications 11(11): 738-748.
[23]	Ishizaka, A., and A. Labib. 2009. Analytic hierarchy process and expert choice: Benefits and limitations. OR Insight 22(4): 201-220.
[24]	Jordan, M.I., and T.M. Mitchell. 2015. Machine learning: Trends, perspectives, and prospects. Science 349(6245): 255-260.
[25]	Kotsiantis, S.B. 2013. Decision trees: A recent overview. Artificial Intelligence Review 39(4): 261-283.
[26]	Li, Y., F.B. Osei, T. Hu, and A. Stein. 2023. Urban flood susceptibility mapping based on social media data in Chengdu City, China. Sustainable Cities and Society 88: Article 104307.
[27]	Liu, H., G. Zhou, R. Wennersten, and B. Frostell. 2014. Analysis of sustainable urban development approaches in China. Habitat International 41: 24-32.
[28]	Liu, S., L. Wang, W. Zhang, Y. He, and S. Pijush. 2023. A comprehensive review of machine learning-based methods in landslide susceptibility mapping. Geological Journal 58(6): 2283-2301.
[29]	Lu, H., X. Lu, L. Jiao, and Y. Zhang. 2022. Evaluating urban agglomeration resilience to disaster in the Yangtze Delta city group in China. Sustainable Cities and Society 76: Article 103464.
[30]	Lyu, H.-M., and Z.-Y. Yin. 2023. An improved MCDM combined with GIS for risk assessment of multi-hazards in Hong Kong. Sustainable Cities and Society 91: Article 104427.
[31]	Machowski, R., M.A. Rzetala, M. Rzetala, and M. Solarski. 2016. Geomorphological and hydrological effects of subsidence and land use change in industrial and urban areas. Land Degradation & Development 27(7): 1740-1752.
[32]	Marcílio, W.E., and D.M. Eler. 2020. From explanations to feature selection: Assessing SHAP values as feature selection mechanism. In Proceedings of the 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 7-10 November 2020, Porto de Galinhas, Brazil, 340-347.
[33]	Medar, R., V.S. Rajpurohit, and B. Rashmi. 2017. Impact of training and testing data splits on accuracy of time series forecasting in machine learning. In Proceedings of the 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 17-18 August 2017, Pune, India, 1-6.
[34]	Miller, D.J., and J. Sias. 1998. Deciphering large landslides: Linking hydrological, groundwater and slope stability models through GIS. Hydrological Processes 12(6): 923-941.
[35]	Munier, N., E. Hontoria, N. Munier, and E. Hontoria. 2021. Shortcomings of the AHP method. In Uses and limitations of the AHP method: A non-mathematical and rational analysis, ed. N. Munier, and E. Hontoria, 41-90. Cham: Springer.
[36]	Papadopoulou-Vrynioti, K., G.D. Bathrellos, H.D. Skilodimou, G. Kaviris, and K. Makropoulos. 2013. Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Engineering Geology 158: 77-88.
[37]	Pham, B.T., T. Nguyen-Thoi, C. Qi, T. Van Phong, J. Dou, L.S. Ho, H. Van Le, and I. Prakash. 2020. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195: Article 104805.
[38]	Pourghasemi, H.R., A. Gayen, M. Edalat, M. Zarafshar, and J.P. Tiefenbacher. 2020. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?. Geoscience Frontiers 11(4): 1203-1217.
[39]	Pradhan, B., S. Lee, A. Dikshit, and H. Kim. 2023. Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model. Geoscience Frontiers 14(6): Article 101625.
[40]	Prokhorenkova, L., G. Gusev, A. Vorobev, A.V. Dorogush, and A. Gulin. 2018. CatBoost: Unbiased boosting with categorical features. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 3-8 December 2018, Montréal, Canada. https://proceedings.neurips.cc/paper_files/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf. Accessed 13 Jul 2023.
[41]	Redclift, M. 1989. The environmental consequences of Latin America’s agricultural development: Some thoughts on the Brundtland Commission report. World Development 17(3): 365-377.
[42]	Rudin, C. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1(5): 206-215.
[43]	Sharifani, K., and M. Amini. 2023. Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal 10(7): 3897-3904.
[44]	Shi, P., X. Yang, W. Xu, and J. Wang. 2016. Mapping global mortality and affected population risks for multiple natural hazards. International Journal of Disaster Risk Science 7(1): 54-62.
[45]	Shi, P., T. Ye, Y. Wang, T. Zhou, W. Xu, J. Du, J. Wang, and N. Li et al. 2020. Disaster risk science: A geographical perspective and a research framework. International Journal of Disaster Risk Science 11(4): 426-440.
[46]	Shirzaei, M., J. Freymueller, T.E. Törnqvist, D.L. Galloway, T. Dura, and P.S.J. Minderhoud. 2021. Measuring, modelling and projecting coastal land subsidence. Nature Reviews Earth & Environment 2(1): 40-58.
[47]	Storkey, A. 2009. When training and test sets are different: Characterizing learning transfer. In Dataset shift in machine learning, ed. J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N.D. Lawrence. Cambridge, MA: The MIT Press.
[48]	Sun, D. 2024. Land subsidence susceptibility mapping in urban settlements using time-series PS-InSAR and random forest model. Gondwana Research 125: 406-424.
[49]	Tehrany, M.S., B. Pradhan, and M.N. Jebur. 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology 512: 332-343.
[50]	Van den Broeck, G., A. Lykov, M. Schleich, and D. Suciu. 2022. On the tractability of SHAP explanations. Journal of Artificial Intelligence Research 74: 851-886.
[51]	Wang, Y., S. Li, X. Liu, J. Zhang, and W. Cheng. 2020. Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers & Geosciences 138: Article 104445.
[52]	Wang, K., J. Zhang, G. Gao, J. Qiu, Y. Zhong, C. Guo, W. Zhao, K. Tang, and X. Su. 2022. Causes, risk analysis, and countermeasures of urban road collapse in China from 2019 to 2020. Journal of Performance of Constructed Facilities 36(6): Article 04022054.
[53]	Wang, Y., Y. Qiao, W. Deng, F. Wang, W. Bai, J. Jiang, J. Liu, S. Xu, et al. 2023. Construction of an urban road collapse risk assessment model and its case study in Guangzhou. In Computational and experimental simulations in engineering: Proceedings of the International Conference on Computational & Experimental Engineering and Sciences 2023, ed. S. Li, 269-290. Cham: Springer.
[54]	Wang, J., and D. He. 2015. Sustainable urban development in China: Challenges and achievements. Mitigation and Adaptation Strategies for Global Change 20(5): 665-682.
[55]	Wang, X.-W., and Y.-S. Xu. 2022. Investigation on the phenomena and influence factors of urban ground collapse in China. Natural Hazards 113(1): 1-33.
[56]	Wei, A., D. Li, Y. Zhou, Q. Deng, and L. Yan. 2021. A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model. Natural Hazards 105(1): 405-430.
[57]	Wu, Y., X. Jiang, Z. Guan, W. Luo, and Y. Wang. 2018. AHP-based evaluation of the karst collapse susceptibility in Tailai Basin, Shandong Province, China. Environmental Earth Sciences 77: 1-14.
[58]	Yan, G., D. Lu, S. Li, S. Liang, L. Xiong, and G. Tang. 2024. Optimizing slope unit-based landslide susceptibility mapping using the priority-flood flow direction algorithm. Catena 235: Article 107657.
[59]	Yang, Y., Y. Yuan, Z. Han, and G. Liu. 2022. Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach. Indoor Air 32(2): Article e12984.
[60]	Yu, L., Y. Wang, and B. Pradhan. 2024. Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China. Geoscience Frontiers 15(4): Article 101802.
[61]	Zhang, Z., and Y. Li. 2020. Coupling coordination and spatiotemporal dynamic evolution between urbanization and geological hazards - A case study from China. Science of the Total Environment 728: Article 138825.
[62]	Zhang, H., and Z. Wang. 2022. Human activities and natural geographical environment and their interactive effects on sudden geologic hazard: A perspective of macro-scale and spatial statistical analysis. Applied Geography 143: Article 102711.
[63]	Zhou, Y., T. Wu, and Y. Wang. 2022. Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: A case study of Hangzhou. Science of the Total Environment 807: Article 150813.
[64]	Zhou, N.-Q., and S. Zhao. 2013. Urbanization process and induced environmental geological hazards in China. Natural Hazards 67: 797-810.