Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3087-2025
© Author(s) 2025. 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-25-3087-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Sep 2025
Research article |
| 05 Sep 2025
| 05 Sep 2025
Evaluating Yangtze River Delta Urban Agglomeration flood risk using a hybrid method of automated machine learning and analytic hierarchy process
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Haipeng Lu
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Yaru Zhang
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Hengxu Jin
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Shuai Wu
Lianyungang Real Estate Registry, Lianyungang 222006, China
Yixuan Gao
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Shuliang Zhang
CORRESPONDING AUTHOR
Key Laboratory of VGE of the Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
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Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422, https://doi.org/10.5194/hess-24-5407-2020, https://doi.org/10.5194/hess-24-5407-2020, 2020
Short summary
Short summary
Rainfall is a driving force that accounts for a large proportion of soil loss around the world. Most previous studies used a fixed rainfall–energy relationship to estimate rainfall energy, ignoring the spatial and temporal changes of raindrop microphysical processes. This study proposes a novel method for large-scale and long-term rainfall energy and rainfall erosivity investigations based on rainfall microphysical parameterization schemes in the Weather Research and Forecasting (WRF) model.
Cited articles
Abu-Salih, B., Wongthongtham, P., Coutinho, K., Qaddoura, R., Alshaweesh, O., and Wedyan, M.: The development of a road network flood risk detection model using optimised ensemble learning, Eng. Appl. Artif. Intel., 122, 106081, https://doi.org/10.1016/j.engappai.2023.106081, 2023.
Adnan, R. M., Yuan, X., Kisi, O., and Anam, R.: Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm, Adv. Meteorol., 2017, 1–23, https://doi.org/10.1155/2017/2391621, 2017.
Anon: 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.
Aven, T.: Risk assessment and risk management: Review of recent advances on their foundation, Eur. J. Oper. Res., 253, 1–13, https://doi.org/10.1016/j.ejor.2015.12.023, 2016.
Bostan, P. A., Heuvelink, G. B. M., and Akyurek, S. Z.: Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey, Int. J. Appl. Earth Obs., 19, 115–126, https://doi.org/10.1016/j.jag.2012.04.010, 2012.
Consuegra-Ayala, J. P., Gutiérrez, Y., Almeida-Cruz, Y., and Palomar, M.: Intelligent ensembling of auto-ML system outputs for solving classification problems, Inform. Sciences, 609, 766–780, https://doi.org/10.1016/j.ins.2022.07.061, 2022.
Cressie, N.: The origins of kriging, Math. Geol., 22, 239–252, https://doi.org/10.1007/BF00889887, 1990.
Criado, M., Martínez-Graña, A., San Román, J. S., and Santos-Francés, F.: Flood risk evaluation in urban spaces: The study case of Tormes River (Salamanca, Spain), Int. J. Env. Res. Pub. He., 16, 5, https://doi.org/10.3390/ijerph16010005, 2019.
Ding, T., Chen, J., Fang, Z., and Chen, J.: Assessment of coordinative relationship between comprehensive ecosystem service and urbanization: A case study of Yangtze River Delta urban Agglomerations, China, Ecol. Indic., 133, 108454, https://doi.org/10.1016/j.ecolind.2021.108454, 2021.
Donegan, H. A., Dodd, F. J., and McMaster, T. B. M.: A New Approach to Ahp Decision-Making, J. Roy. Stat. Soc. D-Sta., 41, 295–302, https://doi.org/10.2307/2348551, 1992.
Echendu, A. J.: The impact of flooding on Nigeria's sustainable development goals (SDGs), Ecosyst. Health Sustainability, 6, 1791735, https://doi.org/10.1080/20964129.2020.1791735, 2020.
Fernández, D. S. and Lutz, M. A.: Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis, Eng. Geol., 111, 90–98, 2010.
Gain, A. K., Mojtahed, V., Biscaro, C., Balbi, S., and Giupponi, C.: An integrated approach of flood risk assessment in the eastern part of Dhaka City, Nat. Hazards, 79, 1499–1530, https://doi.org/10.1007/s11069-015-1911-7, 2015.
Gu, C., Hu, L., Zhang, X., Wang, X., and Guo, J.: Climate change and urbanization in the Yangtze River Delta, Habitat Int., 35, 544–552, https://doi.org/10.1016/j.habitatint.2011.03.002, 2011.
Guan, X., Yu, F., Xu, H., Li, C., and Guan, Y.: Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach, Sustain. Cities Soc., 109, 105546, https://doi.org/10.1016/j.scs.2024.105546, 2024.
Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., and Blaschke, T.: Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory, J. Hydrol., 590, 125275, https://doi.org/10.1016/j.jhydrol.2020.125275, 2020.
Guo, Y., Quan, L., Song, L., and Liang, H.: Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms, J. Hydrol., 605, 127367, https://doi.org/10.1016/j.jhydrol.2021.127367, 2022.
He, X., Zhao, K., and Chu, X.: AutoML: A survey of the state-of-the-art, Knowl.-Based Syst., 212, 106622, https://doi.org/10.1016/j.knosys.2020.106622, 2021.
Hites, R., De Smet, Y., Risse, N., Salazar-Neumann, M., and Vincke, P.: About the applicability of MCDA to some robustness problems, Eur. J. Oper. Res., 174, 322–332, https://doi.org/10.1016/j.ejor.2005.01.031, 2006.
Hsiao, S.-C., Chiang, W.-S., Jang, J.-H., Wu, H.-L., Lu, W.-S., Chen, W.-B., and Wu, Y.-T.: Flood risk influenced by the compound effect of storm surge and rainfall under climate change for low-lying coastal areas, Sci. Total Environ., 764, 144439, https://doi.org/10.1016/j.scitotenv.2020.144439, 2021.
Hutter, F., Kotthoff, L., and Vanschoren, J. (Eds.): Automated Machine Learning: Methods, Systems, Challenges, Springer Nature, https://doi.org/10.1007/978-3-030-05318-5, 2019.
Jordan, M. and Mitchell, T.: Machine learning: Trends, perspectives, and prospects, Science, 349, 255–260, https://doi.org/10.1126/science.aaa8415, 2015.
Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., and Nasseri, M.: A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method, J. Hydrol., 572, 17–31, https://doi.org/10.1016/j.jhydrol.2019.02.034, 2019.
Kazienko, P., Lughofer, E., and Trawinski, B.: Editorial on the special issue “Hybrid and ensemble techniques in soft computing: recent advances and emerging trends,” Soft Comput., 19, 3353–3355, https://doi.org/10.1007/s00500-015-1916-x, 2015.
Khadka, D., Babel, M. S., and Kamalamma, A. G.: Assessing the Impact of Climate and Land-Use Changes on the Hydrologic Cycle Using the SWAT Model in the Mun River Basin in Northeast Thailand, Water, 15, 3672, https://doi.org/10.3390/w15203672, 2023.
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H. L., Hong, H., Chapi, K., and Prakash, I.: A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods, J. Hydrol., 573, 311–323, https://doi.org/10.1016/j.jhydrol.2019.03.073, 2019.
Lang, M., Barriendos, M., Llasat, M. C., Francés, F., Ouarda, T., Thorndycraft, V., Enzel, Y., Bardossy, A., Coeur, D., and Bobée, B.: Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation. Review of Scientific Methods, Nat. Hazards, 31, 623–643, https://doi.org/10.1023/B:NHAZ.0000024895.48463.eb, 2004.
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H.: Feature Selection: A Data Perspective, ACM Comput. Surv., 50, 94:1–94:45, https://doi.org/10.1145/3136625, 2017.
Lin, L., Wu, Z., and Liang, Q.: Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework, Nat. Hazards, 97, 455–475, https://doi.org/10.1007/s11069-019-03615-2, 2019.
Liu, J. and Zhang, B.: Progress of Rainstorm Flood Risk Assessment, Geographical Science, 35, 346–351, https://doi.org/10.13249/j.cnki.sgs.2015.03.013, 2015.
Lu, H., Lu, X., Jiao, L., and Zhang, Y.: Evaluating urban agglomeration resilience to disaster in the Yangtze Delta city group in China, Sustain. Cities Soc., 76, 103464, https://doi.org/10.1016/j.scs.2021.103464, 2022.
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems, Vol. 30, Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017, Curran Associates, Inc., Red Hook, NY, USA, 4765–4774, https://doi.org/10.48550/arXiv.1705.07874, 2017.
Ma, C., Chen, Z., Zhao, K., Xu, H., and Qi, W.: Improved urban flood risk assessment based on spontaneous-triggered risk assessment conceptual model considering road environment, J. Hydrol., 608, 127693, https://doi.org/10.1016/j.jhydrol.2022.127693, 2022.
Mahmoud, S. H. and Gan, T. Y.: Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping, Sci. Total Environ., 636, 152–167, https://doi.org/10.1016/j.scitotenv.2018.04.282, 2018.
Mei, C., Liu, J., Wang, H., Shao, W., Yang, Z., Huang, Z., Li, Z., and Li, M.: Flood risk related to changing rainfall regimes in arterial traffic systems of the Yangtze River Delta, Anthropocene, 35, 100306, https://doi.org/10.1016/j.ancene.2021.100306, 2021.
Mia, M. U., Rahman, M., Elbeltagi, A., Abdullah-Al-Mahbub, M., Sharma, G., Islam, H. M. T., Pal, S. C., Costache, R., Islam, A. R. M. T., Islam, M. M., Chen, N., Alam, E., and Washakh, R. M. A.: Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology, Geocarto Int., 38, 1–29, https://doi.org/10.1080/10106049.2022.2112982, 2023.
Milanesi, L., Pilotti, M., and Ranzi, R.: A conceptual model of people's vulnerability to floods, Water Resour. Res., 51, 182–197, https://doi.org/10.1002/2014WR016172, 2015.
Mirzaei, S., Vafakhah, M., Pradhan, B., and Alavi, S. J.: Flood susceptibility assessment using extreme gradient boosting (EGB), Iran, Earth Sci. Inform., 14, 51–67, https://doi.org/10.1007/s12145-020-00530-0, 2021.
Mishra, K. and Sinha, R.: Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach, Geomorphology, 350, 106861, https://doi.org/10.1016/j.geomorph.2019.106861, 2020.
Morales-Torres, A., Escuder-Bueno, I., Andrés-Doménech, I., and Perales-Momparler, S.: Decision Support Tool for energy-efficient, sustainable and integrated urban stormwater management, Environ. Model. Softw., 84, 518–528, https://doi.org/10.1016/j.envsoft.2016.07.019, 2016.
Munim, Z. H., Sørli, M. A., Kim, H., and Alon, I.: Predicting maritime accident risk using Automated Machine Learning, Reliab. Eng. Syst. Safe., 248, 110148, https://doi.org/10.1016/j.ress.2024.110148, 2024.
Nagarajah, T. and Poravi, G.: A review on automated machine learning (AutoML) systems, in: Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, 29–31 March 2019, IEEE, Piscataway, NJ, USA, 1–6, https://doi.org/10.1109/I2CT45611.2019.9033810, 2019.
National Meteorological Information Center, China Meteorological Administration (CMA): Hourly precipitation data from 120 meteorological stations, [dataset], available at: https://data.cma.cn/, last access: 3 December 2021, 2021.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., and Ramasastri, K. S.: A neuro-fuzzy computing technique for modeling hydrological time series, J. Hydrol., 291, 52–66, 2004.
Özdemir, H., Baduna Koçyiğit, M., and Akay, D.: Flood susceptibility mapping with ensemble machine learning: a case of Eastern Mediterranean basin, Türkiye, Stoch. Env. Res. Risk A, 37, 4273–4290, https://doi.org/10.1007/s00477-023-02507-z, 2023.
Pham, B. T., Luu, C., Dao, D. V., Phong, T. V., Nguyen, H. D., Le, H. V., von Meding, J., and Prakash, I.: Flood risk assessment using deep learning integrated with multi-criteria decision analysis, Knowl.-Based Syst., 219, 106899, https://doi.org/10.1016/j.knosys.2021.106899, 2021.
Raschka, S.: Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning, arXiv [preprint], arXiv:1811.12808, https://doi.org/10.48550/arXiv.1811.12808, 2020.
Rashidi Shikhteymour, S., Borji, M., Bagheri-Gavkosh, M., Azimi, E., and Collins, T. W.: A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods, Appl. Geogr., 158, 103035, https://doi.org/10.1016/j.apgeog.2023.103035, 2023.
Resource and Environmental Science and Data Center (RESDC): Administrative boundaries and river network density, Chinese Academy of Sciences [data set], https://www.resdc.cn/DataList.aspx (last access: 13 May 2022), 2022.
Saaty, T. L.: The Analytic Hierarchy Process, McGraw-Hill, New York, 287 pp., ISBN 0070543712, 1980.
Saaty, T. L.: The Analytic Hierarchy Process: Decision Making in Complex Environments, in: Quantitative Assessment in Arms Control: Mathematical Modeling and Simulation in the Analysis of Arms Control Problems, edited by: Avenhaus, R. and Huber, R. K., Springer US, Boston, MA, https://doi.org/10.1007/978-1-4613-2805-6_12, 285–308, 1984.
Sarro, F., Moussa, R., Petrozziello, A., and Harman, M.: Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates, IEEE T. Software Eng., 48, 1868–1882, https://doi.org/10.1109/TSE.2020.3040793, 2022.
Scott, D., Hall, C. M., Rushton, B., and Gössling, S.: A review of the IPCC Sixth Assessment and implications for tourism development and sectoral climate action, J. Sustain. Tour., 32, 1725–1742, https://doi.org/10.1080/09669582.2023.2195597, 2023.
Seemuangngam, A. and Lin, H.-L.: The impact of urbanization on urban flood risk of Nakhon Ratchasima, Thailand, Appl. Geogr., 162, 103152, https://doi.org/10.1016/j.apgeog.2023.103152, 2024.
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., and Shirzadi, A.: Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping, J. Environ. Manage., 217, 1–11, 2018.
Shuster, W. D., Bonta, J., Thurston, H., Warnemuende, E., and Smith, D. R.: Impacts of impervious surface on watershed hydrology: A review, Urban Water J., 2, 263–275, https://doi.org/10.1080/15730620500386529, 2005.
Sinha, R., Bapalu, G. V., Singh, L. K., and Rath, B.: Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of Analytical Hierarchy Process (AHP), J. Indian Soc. Remote, 36, 335–349, https://doi.org/10.1007/s12524-008-0034-y, 2008.
Sun, B., Fang, C., Liao, X., Guo, X., and Liu, Z.: The relationship between urbanization and air pollution affected by intercity factor mobility: A case of the Yangtze River Delta region, Environ. Impact Asses., 100, 107092, https://doi.org/10.1016/j.eiar.2023.107092, 2023.
Tang, Z., Wang, P., Li, Y., Sheng, Y., Wang, B., Popovych, N., and Hu, T.: Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980, J. Environ. Manage., 353, 120113, https://doi.org/10.1016/j.jenvman.2024.120113, 2024.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Global Flood Database (GFDB) derived from MODIS imagery, Google Earth Engine Data Catalog, [dataset], available at: https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1, last access: 25 January 2023.
U.S. Geological Survey (USGS): SRTM1 Digital Elevation Data, [dataset], available at: https://earthexplorer.usgs.gov/, last access: 19 January 2023, 2023.
Vincent, A. M., Parthasarathy K. S. S., and Jidesh, P.: Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization, Appl. Soft Comput., 148, 110846, https://doi.org/10.1016/j.asoc.2023.110846, 2023.
Wagenaar, D., Curran, A., Balbi, M., Bhardwaj, A., Soden, R., Hartato, E., Mestav Sarica, G., Ruangpan, L., Molinario, G., and Lallemant, D.: Invited perspectives: How machine learning will change flood risk and impact assessment, Nat. Hazards Earth Syst. Sci., 20, 1149–1161, https://doi.org/10.5194/nhess-20-1149-2020, 2020.
Wan, H., Zhong, Z., Yang, X., and Li, X.: Impact of city belt in Yangtze River Delta in China on a precipitation process in summer: A case study, Atmos. Res., 125–126, 63–75, https://doi.org/10.1016/j.atmosres.2013.02.004, 2013.
Wang, M., Fu, X., Zhang, D., Chen, F., Liu, M., Zhou, S., Su, J., and Tan, S. K.: Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways, Sci. Total Environ., 880, 163470, https://doi.org/10.1016/j.scitotenv.2023.163470, 2023a.
Wang, P., Deng, X., Zhou, H., and Qi, W.: Responses of urban ecosystem health to precipitation extreme: A case study in Beijing and Tianjin, J. Clean. Prod., 177, 124–133, https://doi.org/10.1016/j.jclepro.2017.12.125, 2018a.
Wang, P., Li, Y., Yu, P., and Zhang, Y.: The analysis of urban flood risk propagation based on the modified susceptible infected recovered model, J. Hydrol., 603, 127121, https://doi.org/10.1016/j.jhydrol.2021.127121, 2021.
Wang, T., Wang, H., Wang, Z., and Huang, J.: Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China, J. Environ. Manage., 345, 118787, https://doi.org/10.1016/j.jenvman.2023.118787, 2023b.
Wang, Y., Liu, G., Guo, E., and Yun, X.: Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions, Water, 10, 1229, https://doi.org/10.3390/w10091229, 2018b.
Wang, Y., Li, C., Hu, Y., Lv, J., Liu, M., Xiong, Z., and Wang, Y.: Evaluation of urban flooding and potential exposure risk in central and southern Liaoning urban agglomeration, China, Ecol. Indic., 154, 110845, https://doi.org/10.1016/j.ecolind.2023.110845, 2023c.
Webb, G. I. and Zheng, Z.: Multistrategy ensemble learning: reducing error by combining ensemble learning techniques, IEEE T. Knowl. Data En., 16, 980–991, https://doi.org/10.1109/TKDE.2004.29, 2004.
Webster, R. and Oliver, M. A.: Geostatistics for Environmental Scientists, John Wiley & Sons, 333 pp., https://doi.org/10.1002/9780470517277, 2007.
Wolpert, D. H. and Macready, W. G.: No free lunch theorems for optimization, IEEE T. Evolut. Comput., 1, 67–82, https://doi.org/10.1109/4235.585893, 1997.
Xu, H., Hou, X., Pan, S., Bray, M., and Wang, C.: Socioeconomic impacts from coastal flooding in the 21st century China's coastal zone: A coupling analysis between coastal flood risk and socioeconomic development, Sci. Total Environ., 917, 170187, https://doi.org/10.1016/j.scitotenv.2024.170187, 2024.
Yan, M., Yang, J., Ni, X., Liu, K., Wang, Y., and Xu, F.: Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China, J. Hydrol., 630, 130695, 2024.
Yang, J. and Huang, X.: The 30 m annual land cover datasets and its dynamics in China from 1985 to 2022, Earth Syst. Sci. Data, Zenodo, [dataset], https://doi.org/10.5281/zenodo.8176941, last access: 6 October 2023, 2023.
Yang, J., Zeng, X., Zhong, S., and Wu, S.: Effective Neural Network Ensemble Approach for Improving Generalization Performance, IEEE T. Neur. Net. Lear., 24, 878–887, https://doi.org/10.1109/TNNLS.2013.2246578, 2013.
Yang, W., Xu, K., Lian, J., Ma, C., and Bin, L.: Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods, Ecol. Indic., 89, 269–280, https://doi.org/10.1016/j.ecolind.2018.02.015, 2018.
Yang, X., Li, H., Zhang, J., Niu, S., and Miao, M.: Urban economic resilience within the Yangtze River Delta urban agglomeration: Exploring spatially correlated network and spatial heterogeneity, Sustain. Cities Soc., 103, 105270, https://doi.org/10.1016/j.scs.2024.105270, 2024.
Yuan, X., Chen, C., Lei, X., Yuan, Y., and Muhammad Adnan, R.: Monthly runoff forecasting based on LSTM–ALO model, Stoch. Env. Res. Risk A, 32, 2199–2212, https://doi.org/10.1007/s00477-018-1560-y, 2018.
Zhang, H., Wu, C., Chen, W., and Huang, G.: Assessing the Impact of Climate Change on the Waterlogging Risk in Coastal Cities: A Case Study of Guangzhou, South China, 18, 1549–1562, https://doi.org/10.1175/JHM-D-16-0157.1, 2017.
Short summary
This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA), where we determined flood risk assessment indices across different dimensions, including hazard, exposure, vulnerability, and resilience. We constructed a flood risk assessment model using automated machine learning and the analytic hierarchy process to examine the spatial and temporal changes in flood risk in the region over the past 30 years (1990 to 2020), aiming to provide a scientific basis for flood prevention and resilience strategies in the YRDUA.
This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA), where we determined...
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