Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-5421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-data source based quantifying urban flood severity in major Chinese cities (2000–2024) using a hybrid machine-learning weighting framework
Guoqiang Peng
School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
Yujing Sun
CORRESPONDING AUTHOR
School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
Mao Wang
School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Fengyuan Zhang
School of Geography, Nanjing Normal University, Nanjing 210023, China
Related authors
No articles found.
Yucheng Shu, Min Chen, and Guonian Lv
Abstr. Int. Cartogr. Assoc., 10, 262, https://doi.org/10.5194/ica-abs-10-262-2025, https://doi.org/10.5194/ica-abs-10-262-2025, 2025
Yuqian Hu, Heng Li, Chunxiao Zhang, Dingtao Shen, Bingli Xu, Min Chen, Wenhao Chu, and Rongrong Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-1161, https://doi.org/10.5194/egusphere-2025-1161, 2025
Preprint archived
Short summary
Short summary
Our study developed a preliminary dataset of catchment attributes and meteorological variables covering 544 basins in China, and evaluated the applicability of process-based models, the long short-term memory model, and hybrid modeling methods. Results highlight the critical role of meteorological data quality and the potential of hybrid approaches. Our findings support model selection and offer reference for regions with limited observational data.
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, and Jinyue Yan
Earth Syst. Sci. Data, 14, 4057–4076, https://doi.org/10.5194/essd-14-4057-2022, https://doi.org/10.5194/essd-14-4057-2022, 2022
Short summary
Short summary
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Cited articles
Ahmad, I., Ping, W., Ullah, S., Faqeih, K. Y., Alamri, S. M., Alamery, E. R., Abalkhail, A. A. A., and Bilal Jan, H. M.: Spatiotemporal Mapping of Urban Flood Susceptibility: A Multi-Criteria GIS-Based Assessment in Nangarhar, Afghanistan, Land, 14, 2376, https://doi.org/10.3390/land14122376, 2025.
Ali, Y., Afnan, J., Jannat, M., and Uddin, M. M.: Assessing alteration of urban floodplains using remote sensing and GIS: a three decade analysis (1992–2022), Geol. Ecol. Landsc., 1–16, https://doi.org/10.1080/24749508.2025.2524209, 2025.
Bagheri, A. and Liu, G.-J.: Climate change and urban flooding: assessing remote sensing data and flood modeling techniques: a comprehensive review, Environ. Rev., 33, 1–14, https://doi.org/10.1139/er-2024-0065, 2025.
Ban, J., Sutton, C., Ma, Y. Lin, C., and Chen, K.: Association of flooding exposure with cause-specific mortality in North Carolina, United States, Nat. Water, 1, 1027–1034, https://doi.org/10.1038/s44221-023-00167-5, 2023.
Bhusal, A., Thakur, B., Kalra, A., Benjankar, R., and Shrestha, A.: Evaluating the Effectiveness of Best Management Practices in Adapting the Impacts of Climate Change-Induced Urban Flooding, Atmosphere, 15, 281, https://doi.org/10.3390/atmos15030281, 2024.
Cao, W., Zhou, Y., Güneralp, B., Li, X., Zhao, K., and Zhang, H.: Increasing global urban exposure to flooding: An analysis of long-term annual dynamics, Sci. Total Environ., 817, 153012, https://doi.org/10.1016/j.scitotenv.2022.153012, 2022.
Chen, J., Li, Y., Zhang, L., and Tian, Y.: Impact of Interactions between Different Rainfall Patterns and River Water Levels on Urban Flooding in Inland Plain Cities, Water Resour. Manag., 39, 7515–7539, https://doi.org/10.1007/s11269-025-04306-3, 2025.
Chen, J., Gao, C., Zhou, H., Wang, Q., She, L., Qing, D., and Cao, C.: Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods, Appl. Sci., 14, 3694, https://doi.org/10.3390/app14093694, 2024.
Dai, D., Bo, M., Ren, X., and Dai, K.: Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review, Ecol. Indic., 158, 111565, https://doi.org/10.1016/j.ecolind.2024.111565, 2024.
Duan, C., Zheng, X., Li, R., and Wu, Z.: Urban flood vulnerability Knowledge-Graph based on remote sensing and textual bimodal data fusion, J. Hydrol., 633, 131010, https://doi.org/10.1016/j.jhydrol.2024.131010, 2024.
Fang, D., Xu, F., Jin, X., Song, C., Gao, P., Sun, L., Wang, D., and Feng, K.: Stress-testing the cascading economic impacts of urban flooding across 306 Chinese cities, Nat. Cities, 3, 89–101, https://doi.org/10.1038/s44284-025-00372-1, 2026.
Fu, S., Schultz, D. M., Lyu, H., Zheng, Z., and Zhang, C.: Creating a national urban flood dataset for China from news texts (2000–2022) at the county level, Hydrol. Earth Syst. Sci., 29, 767–783, https://doi.org/10.5194/hess-29-767-2025, 2025.
Ge, H. and Zhu, L.: Extreme flood levels during the operation of cascade reservoirs: A case study of the Lower Yangtze River in 2020, Water, 15, 851, https://doi.org/10.3390/w15050851, 2023.
Ghosh, P., Sudarsan, J. S., and Nithiyanantham, S.: Nature-Based Disaster Risk Reduction of Floods in Urban Areas, Water Resour. Manag., 38, 1847–1866, https://doi.org/10.1007/s11269-024-03757-4, 2024.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Hu, Y., Connor, D. S., Stuhlmacher, M., Peng, J., and Turner II, B. L.: More urbanization, more polarization: evidence from two decades of urban expansion in China, npj Urban Sustain., 4, 33, https://doi.org/10.1038/s42949-024-00170-z, 2024.
Hussain, M., Tayyab, M., Ullah, K., Ullah, S., Rahman, Z. U., Zhang, J., and Al-Shaibah, B.: Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis, Urban Clim., 50, 101589, https://doi.org/10.1016/j.uclim.2023.101589, 2023.
Jiang, Y., Zevenbergen, C., and Ma, Y.: Urban pluvial flooding and stormwater management: A contemporary review of China's challenges and “sponge cities” strategy, Environ. Sci. Policy, 80, 132–143, https://doi.org/10.1016/j.envsci.2017.11.016, 2018.
Kong, F., Sun, S., and Wang, Y.: Comprehensive understanding the disaster-causing mechanism, governance dilemma and targeted countermeasures of urban pluvial flooding in china, Water, 13, 1762, https://doi.org/10.3390/w13131762, 2021.
Li, C., Sun, N., Lu, Y., Guo, B., Wang, Y., Sun, X., and Yao, Y.: Review on Urban Flood Risk Assessment, Sustainability, 15, 765, https://doi.org/10.3390/su15010765, 2023a.
Li, G., Liu, J., and Shao, W.: Urban flood risk assessment under rapid urbanization in Zhengzhou City, China, Reg. Sustain., 4, 332–348, https://doi.org/10.1016/j.regsus.2023.08.004, 2023b.
Li, Y., Wang, L., Li, F., Peng, S. G., and Ding, C.: Quantitative estimation of urban flood damage from storm surges for a coastal city, Nat. Hazards, 121, 16915–16934, https://doi.org/10.1007/s11069-025-07456-0, 2025.
Liang, P. and Ding, Y.: The long-term variation of extreme heavy precipitation and its link to urbanization effects in Shanghai during 1916–2014, Adv. Atmos. Sci., 34, 321–334, https://doi.org/10.1007/s00376-016-6120-0, 2017.
Liu, X., Zhou, P., Lin, Y., Sun, S., Zhang, H., Xu, W., and Yang, S.: Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms, Int. J. Environ. Res. Public Health, 19, 16544, https://doi.org/10.3390/ijerph192416544, 2022.
Luo, Y., Zhang, J., Yu, M., Liang, X., Xia, R., Gao, Y., Gao, X., and Yin, J.: On the influences of urbanization on the extreme rainfall over Zhengzhou on 20 July 2021: A convection-permitting ensemble modeling study, Adv. Atmos. Sci., 40, 393–409, https://doi.org/10.1007/s00376-022-2048-8, 2023.
Misra, A., White, K., Nsutezo, S. F., Straka III., W., and Lavista, J.: Mapping global floods with 10 years of satellite radar data, Nat. Commun., 16, 5762, https://doi.org/10.1038/s41467-025-60973-1, 2025.
Peng, J. and Zhang, J.: Urban flooding risk assessment based on GIS-game theory combination weight: A case study of Zhengzhou City, Int. J. Disast. Risk Re., 77, 103080, https://doi.org/10.1016/j.ijdrr.2022.103080, 2022.
Peng, G., Sun, Y., Wang, M., Chen, M., and Zhang, F.: lianyi732-hue/flood_project: Code and Data, Version 1.0.0, Zenodo [code, data set], https://doi.org/10.5281/zenodo.23681197, 2026.
Qi, S., Cao, S., Hu, S., and Liu, Q.: Bibliometric analysis on urban flood and waterlogging disasters during the period of 1998–2022, Nat. Hazards, 120, 12595–12612, https://doi.org/10.1007/s11069-024-06710-1, 2024.
Rentschler, J., Avner, P., Marconcini, M., Su, R., Strano, E., Vousdoukas, M., and Hallegatte, S.: Global evidence of rapid urban growth in flood zones since 1985, Nature, 622, 87–92, https://doi.org/10.1038/s41586-023-06468-9, 2023.
Romali, N. S., Sulong, S., and Kawasaki, A.: A Systematic Review of Flood Damage Assessment: Insight for the Data-Scarce Regions, Water Resour. Manag., 39, 4707–4734, https://doi.org/10.1007/s11269-025-04265-9, 2025.
Rogers, J. S., Maneta, M. P., Sain, S. R. Madaus, L. E., and Hacker, J. P.: The role of climate and population change in global flood exposure and vulnerability, Nat. Commun., 16, 1287, https://doi.org/10.1038/s41467-025-56654-8, 2025.
Sanders, B. F., Schubert, J. E., Kahl, D. T., Mach, K. J., Brady, D., AghaKouchak, A., Forman, F., Matthew, R. A., Ulibarri, N., and Davis, S. J.: Large and inequitable flood risks in Los Angeles, California, Nat. Sustain., 6, 47–57, https://doi.org/10.1038/s41893-022-00977-7, 2023.
Song, X., Xu, N., Zhang, J., and He, R.: Urban flooding in China: current status, causes and challenges, Adv. Water Sci., 35, 357–373, https://doi.org/10.14042/j.cnki.32.1309.2024.03.001, 2024.
Tan, L. and Schultz, D. M.: Damage classification and recovery analysis of the Chongqing, China, floods of August 2020 based on social-media data, J. Clean. Prod., 313, 127882, https://doi.org/10.1016/j.jclepro.2021.127882, 2021.
Tellman, B., Sullivan, J. A., Kuhn, C. Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging reveals increased proportion of population exposed to floods, Nature, 596, 80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Wang, H., Meng, Y., Xu, H., Wang, H., Guan, X., Liu, Y., Liu, M., and Wu, Z.: Prediction of flood risk levels of urban flooded points though using machine learning with unbalanced data, J. Hydrol., 630, 130742, https://doi.org/10.1016/j.jhydrol.2024.130742, 2024.
Wang, P., Zhu, Y., and Yu, P.: Assessment of Urban Flood Vulnerability Using the Integrated Framework and Process Analysis: A Case from Nanjing, China, Int. J. Environ. Res. Pub. He., 19, 16595, https://doi.org/10.3390/ijerph192416595, 2022a.
Wang, Y., Li, C., Liu, M., Cui, Q., Wang, H., Lv, J., Li, B., Xiong, Z., and Hu, Y.: Spatial characteristics and driving factors of urban flooding in chinese megacities, J. Hydrol., 613, 128464, https://doi.org/10.1016/j.jhydrol.2022.128464, 2022b.
Wing, O. E. J., Pinter, N., Bates, P. D., and Kousky, C.: New insights into US flood vulnerability revealed from flood insurance big data, Nat. Commun., 11, 1444, https://doi.org/10.1038/s41467-020-15264-2, 2020.
Wu, M., Wu, Z., Ge, W., Wang, H., Shen, Y., and Jiang, M.: Identification of sensitivity indicators of urban rainstorm flood disasters: A case study in China, J. Hydrol., 599, 126393, https://doi.org/10.1016/j.jhydrol.2021.126393, 2021.
Wu, Z., Shen, Y., Wang, H., and Wu, M.: An ontology-based framework for heterogeneous data management and its application for urban flood disasters, Earth Sci. Inform., 13, 377–390, https://doi.org/10.1007/s12145-019-00439-3, 2020.
Xu, T., Xie, Z., Jiang, F., Yang, S., Deng, Z., Zhao, L., Wen, G., and Du, Q.: Urban flooding resilience evaluation with coupled rainfall and flooding models: a small area in Kunming City, China as an example, Water Sci. Technol., 87, 2820–2839, https://doi.org/10.2166/wst.2023.149, 2023.
Xu, T., Li, E., Samat, A., Wang, S., and Yu, M.: Evaluation of urban flood-prone regions in Chinese cities based on potential flood hazard assessment model, Nat. Hazards, 121, 18743–18763, https://doi.org/10.1007/s11069-025-07536-1, 2025.
Yang, L., Li, J., Kang, A., Li, S., and Feng, P.: The Effect of Nonstationarity in Rainfall on Urban Flooding Based on Coupling SWMM and MIKE21, Water Resour. Manag., 34, 1535–1551, https://doi.org/10.1007/s11269-020-02522-7, 2020.
Yang, W., Zheng, C., Jiang, X., Wang, H., Lian, J., Hu, D., and Zheng, A.: Study on urban flood simulation based on a novel model of SWTM coupling D8 flow direction and backflow effect, J. Hydrol., 621, 129608, https://doi.org/10.1016/j.jhydrol.2023.129608, 2023.
Yin, D., Chen, Y., Jia, H., Wang, Q., Chen, Z., Xu, C., Li, Q., Wang, W., Yang, Y., Fu, G., and Chen, A. S.: Sponge city practice in China: A review of construction, assessment, operational and maintenance, J. Clean. Prod., 280, 124963, https://doi.org/10.1016/j.jclepro.2020.124963, 2021.
Yin, J., Yu, D., Yin, Z., Liu, M., and He, Q.: Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China, J. Hydrol., 537, 138–145, https://doi.org/10.1016/j.jhydrol.2016.03.037, 2016.
Zhang, H., Jia, H., Liu, W., Wang, J., Xu, D., Li, S., and Liu, X.: Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data, Remote Sens.-Basel, 15, 4301, https://doi.org/10.3390/rs15174301, 2023.
Zhao, J., Zhang, C., Wang, J., Abbas, Z., and Zhao, Y.: Machine learning and SHAP-based susceptibility assessment of storm flood in rapidly urbanizing areas: a case study of Shenzhen, China, Geomat. Nat. Haz. Risk, 15, https://doi.org/10.1080/19475705.2024.2311889, 2024.
Zhang, Q., Li, C., Wen, D., Kang, J., Chen, T., Zhang, B., Hu, Y., and Yin, J.: Global South shows higher urban flood exposures than the Global North under current and future scenarios, Commun. Earth Environ., 6, 594, https://doi.org/10.1038/s43247-025-02585-7, 2025.
Short summary
This study combines official records, news reports, social media posts, and search data to track urban flood events in twenty major Chinese cities from 2000 to 2024. It builds a comparable database and uses an interpretable machine learning framework to measure flood severity across cities and years. Results show a clear north south contrast: southern river and coastal cities face frequent severe floods, while northern cities usually face lower risk but suffer sudden extremes during rare storms.
This study combines official records, news reports, social media posts, and search data to track...
Altmetrics
Final-revised paper
Preprint