Prediction and Machine Learning Analysis of Urban Waterlogging Risks in High-Density Areas From the Perspective of the Built Environment: A Case Study of Shenzhen, China

Highlights

	● Proposes a comprehensive research framework combining LightGBM model and the interpretability algorithm of SHAP, and predicts waterlogging depth and its risk level in urban areas
	● Verifies that historical downtowns in high-density cities face higher risks of waterlogging during extreme rainfall events with machine learning methods
	● Presents a novel exploration in high-density urban context that analyzes the influencing factors and intrinsic mechanisms of urban waterlogging, focusing on hydro-meteorological, urban surface, and architectural configuration factor

Abstract

With the continuous advance of big data and artificial intelligence technologies, various data-driven machine learning algorithms have been widely applied in the studies of urban resilience, particularly in addressing the challenging issue of urban waterlogging. Currently, it is a pressing task to understand the influencing factors of waterlogging from the perspective of built environment, and provide guidance on dynamic monitoring and early alarm services. Focusing on Shenzhen, China, a typical high-density urbanized city, this research constructed a multifactorial dataset encompassing hydrological, meteorological, urban morphology, and waterlogging event data. Then, this research assessed and compared the performance of four mainstream machine learning models—LightGBM, RF, SVR, and BPDNN—in predicting urban waterlogging risks. The results showed that LightGBM had the best accuracy and robustness in predicting waterlogging depths and risk levels in urban areas. The research also employed interpretability algorithm—Shapley Additive Explanations (SHAP)—for decoupling analysis. The results indicated that hydro-meteorological factors (the total rainfall volume and the rainfall lasting time) and several architectural configuration factors (e.g., density of buildings, building congestion degree) are the main influencing factors. In addition, the percentage of water body is vital to waterlogging regulation and retention, especially exhibiting a significant mitigating effect when exceeding 2.5%. This research provides a new technical method for urban waterlogging prediction and reveals the influencing factors and intrinsic mechanisms from the perspective of built environment, which is of great significance for the enhancement of the resilience of high-density cities.

Graphical abstract

Keywords

Urban Waterlogging / Machine Learning / Model Performance Evaluation / Comparative Research / Model Interpretability Analysis / High-Density City

References

[1] Jha, A. K., Miner, T. W., & Stanton-Geddes, Z. (Eds.). (2013). Building Urban Resilience: Principles, Tools, and Practice. The World Bank.

[2] Arabameri, A. , Saha, S. , Chen, W. , Roy, J. , Pradhan, B. , & Bui, D. T. (2020) Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. Journal of Hydrology, ( 587), 125007.

[3] Rafiei-Sardooi, E. , Azareh, A. , Choubin, B. , Mosavi, A. H. , & Clague, J. J. (2021) Evaluating urban flood risk using hybrid method of TOPSIS and machine learning. International Journal of Disaster Risk Reduction, ( 66), 102614.

[4] Wang, Z. , Lai, C. , Chen, X. , Yang, B. , Zhao, S. , & Bai, X. (2015) Flood hazard risk assessment model based on random forest. Journal of Hydrology, ( 527), 1130– 1141.

[5] Chen, J. , Huang, G. , & Chen, W. (2021) Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models. Journal of Environmental Management, ( 293), 112810.

[6] Mei, C. , Liu, J. , Wang, H. , Yang, Z. , Ding, X. , & Shao, W. (2018) Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed. Science of the Total Environment, ( 639), 1394– 1407.

[7] Shafizadeh-Moghadam, H. , Valavi, R. , Shahabi, H. , Chapi, K. , & Shirzadi, A. (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of Environmental Management, ( 217), 1– 11.

[8] Guo, Y. , Quan, L. , Song, L. , & Liang, H. (2022) Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms. Journal of Hydrology, ( 605), 127367.

[9] Wu, Z. , Zhou, Y. , Wang, H. , & Jiang, Z. (2020) Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Science of the Total Environment, ( 716), 137077.

[10] Gan, M. , Pan, S. , Chen, Y. , Cheng, C. , Pan, H. , & Zhu, X. (2021) Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River. Journal of Marine Science and Engineering, 9 ( 5), 496.

[11] Breiman, L. (2001) Random forests. Machine Learning, ( 45), 5– 32.

[12] Panahi, M. , Dodangeh, E. , Rezaie, F. , Khosravi, K. , Van Le, H. , Lee, M.-J. , Lee, S. , & Pham, B. T. (2021) Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA, ( 199), 105114.

[13] Community Construction and Zoning Office, Bureau of Civil Affairs of Shenzhen Municipality. (2024, April 3). Overview of administrative division information.

[14] Statistics Bureau of Shenzhen Municipality. (2023, May 8). Shenzhen statistical bulletin on 2022 national economic and social development.

[15] Zhou, S. , Liu, Z. , Wang, M. , Gan, W. , Zhao, Z. , & Wu, Z. (2022) Impacts of building configurations on urban stormwater management at a block scale using XGBoost. Sustainable Cities and Society, ( 87), 104235.

[16] Meteorological Bureau of Shenzhen Municipality. (2024, May 15). Climatic profile and seasonal characteristics of Shenzhen.

[17] Ke, Q. , Tian, X. , Bricker, J. , Tian, Z. , Guan, G. , Cai, H. , Huang, X. , Yang, H. , & Liu, J. (2020) Urban pluvial flooding prediction by machine learning approaches—A case study of Shenzhen City, China. Advances in Water Resources, ( 145), 103719.

[18] Hou, J. , Guo, K. , Wang, Z. , Jing, H. , & Li, D. (2017) Numerical simulation of design storm pattern effects on urban flood inundation. Advances in Water Science, 28 ( 6), 820– 828.

[19] Zhou, H. , Liu, J. , Gao, C. , & Ou, S. (2018) Analysis of current situation and problems of urban waterlogging control in China. Journal of Catastrophology, 33 ( 3), 147– 151.

[20] Song, L. , & Xu, Z. (2019) Coupled hydrologic-hydrodynamic model for urban rainstorm water logging simulation: Recent advances. Journal of Beijing Normal University (Natural Science), 55 ( 5), 581– 587.

[21] Wu, J. , & Zhang, P. (2017) The effect of urban landscape pattern on urban waterlogging. Acta Geographica Sinica, 72 ( 3), 444– 456.

[22] Xu, Y. , Li, K. , Xie, Y. , Ling, H. , Qian, M. , Wang, X. , & Lu, Y. (2018) Study on the influencing factors and multiple regression model of urban waterlogging based on GIS—A case study of Shanghai. Journal of Fudan University (Natural Science), 57 ( 2), 182– 198.

[23] Xu, H. , Lu, H. , Zhan, X. , Li, J. , Gao, C. , & Zhang, T. (2024) Impacts of underlying surface changes and rainfall patterns on flooding at airport area in Zhuhai. China Rural Water and Hydropower, , 1– 16.

[24] Shrestha, R. , Di, L. , Eugene, G. Y. , Kang, L. , Shao, Y.-Z. , & Bai, Y.-Q. (2017) Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture, 16 ( 2), 398– 407.

[25] Lin, J. , He, X. , Lu, S. , Liu, D. , & He, P. (2021) Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm. Environmental Research, ( 196), 110438.

[26] Kim, Y. , Eisenberg, D. A. , Bondank, E. N. , Chester, M. V. , Mascaro, G. , & Underwood, B. S. (2017) Fail-safe and safe-to-fail adaptation: Decision-making for urban flooding under climate change. Climatic Change, ( 145), 397– 412.

[27] Wang, J. , Yu, C. W. , & Cao, S.-J. (2022) Urban development in the context of extreme flooding events. Indoor and Built Environment, 31 ( 1), 3– 6.

[28] Wang, M. , Li, Y. , Yuan, H. , Zhou, S. , Wang, Y. , Ikram, R. M. A. , & Li, J. (2023) An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility. Ecological Indicators, ( 156), 111137.

[29] Yan, M. , Yang, J. , Ni, X. , Liu, K. , Wang, Y. , & Xu, F. (2024) Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China. Journal of Hydrology, ( 630), 130695.

[30] Zhang, H. , Zhang, J. , Fang, H. , & Yang, F. (2022) Urban flooding response to rainstorm scenarios under different return period types. Sustainable Cities and Society, ( 87), 104184.

[31] Kumar, R. , & Acharya, P. (2016) Flood hazard and risk assessment of 2014 floods in Kashmir Valley: A space-based multisensor approach. Natural Hazards, ( 84), 437– 464.

[32] Jiang, F. , Xie, Z. , Xu, J. , Yang, S. , Zheng, D. , Liang, Y. , Hou, Z. , & Wang, J. (2023) Spatial and component analysis of urban flood resiliency of Kunming City in China. International Journal of Disaster Risk Reduction, ( 93), 103759.

[33] Xu, Y. , Liu, M. , Hu, Y. , Li, C. , & Xiong, Z. (2019) Analysis of three-dimensional space expansion characteristics in old industrial area renewal using GIS and Barista: A case study of Tiexi District, Shenyang, China. Sustainability, 11 ( 7), 1860.

[34] Cheng, C. , Yu, X. , Guo, S. , & Ma, T. (2005) Analysis of the crowd degree of building for communities based on high spatial resolution remote sensed images. Acta Scientiarum Naturalium Universitatis Pekinensis, 41 ( 6), 875– 881.

[35] Meteorological Bureau of Shenzhen Municipality. (2023, March 30). New version of the storm intensity formula.

[36] Dai, Y. , Wang, Z. , Dai, L. , Cao, Q. , & Wang, T. (2017) Application of Chicago Hyetograph Method in design of short duration rainstorm patterns. Journal of Arid Meteorology, 35 ( 6), 1061– 1069.

[37] Wu, Z. , Qiao, R. , Zhao, S. , Liu, X. , Gao, S. , Liu, Z. , Ao, X. , Zhou, S. , Wang, Z. , & Jiang, Q. (2022) Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning. Science of the Total Environment, ( 838), 156348.

[38] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30. Neural Information Processing Systems Foundation, Inc.

[39] Belgiu, M. , & Drăguţ, L. (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, ( 114), 24– 31.

[40] Wu, J. , Liu, H. , Wei, G. , Song, T. , Zhang, C. , & Zhou, H. (2019) Flash flood forecasting using support vector regression model in a small mountainous catchment. Water, 11 ( 7), 1327.

[41] Wu, J. , Liu, Z. , Liu, T. , Liu, W. , Liu, W. , & Luo, H. (2023) Assessing urban pluvial waterlogging resilience based on sewer congestion risk and climate change impacts. Journal of Hydrology, ( 626), 130230.

[42] Goodwin, P. , & Lawton, R. (1999) On the asymmetry of the symmetric MAPE. International Journal of Forecasting, 15 ( 4), 405– 408.

[43] Hodson, T. O. (2022) Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15 ( 14), 5481– 5487.

[44] Hassija, V. , Chamola, V. , Mahapatra, A. , Singal, A. , Goel, D. , Huang, K. , Scardapane, S. , Spinelli, I. , Mahmud, M. , & Hussain, A. (2023) Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, ( 16), 45– 74.

[45] Li, Z. (2022) Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, ( 96), 101845.

[46] Parsa, A. B. , Movahedi, A. , Taghipour, H. , Derrible, S. , & Mohammadian, A. K. (2020) Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis Prevention, ( 136), 105405.

[47] Van den Broeck, G. , Lykov, A. , Schleich, M. , & Suciu, D. (2022) On the tractability of SHAP explanations. Journal of Artificial Intelligence Research, ( 74), 851– 886.

[48] Molnar, C. (2020). Interpretable machine learning.

[49] Casalicchio, G., Molnar, C., & Bischl, B. (2019). Visualizing the Feature Importance for Black Box Models. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I (pp. 655–670). Springer.

[50] Michiels, J. , Suykens, J. , & De Vos, M. (2024) Explaining the model and feature dependencies by decomposition of the Shapley value. Decision Support Systems, ( 182), 114234.

[51] Qi, W. , Hou, J. , Liu, J. , Han, H. , Guo, K. , & Ma, Y. (2018) Lake control on surface runoff causing urban flood inundation. Journal of Hydroelectric Engineering, 37 ( 9), 8– 18.

[52] Poelmans, L. , Van Rompaey, A. , & Batelaan, O. (2010) Coupling urban expansion models and hydrological models: How important are spatial patterns?. Land Use Policy, 27 ( 3), 965– 975.

Acknowledgements

"Resilience Enhancement and Dynamic Planning of Urban Grey-Green Infrastructure Based on Climate Adaptability," Guangdong Provincial Natural Science Foundation Youth Enhancement Project (No. 2023A1515030158)

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