Volume 14 Issue 2
Apr.  2023
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Yaoxing Liao, Zhaoli Wang, Chengguang Lai, Chong-Yu Xu. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model[J]. International Journal of Disaster Risk Science, 2023, 14(2): 253-268. doi: 10.1007/s13753-023-00481-2
Citation: Yaoxing Liao, Zhaoli Wang, Chengguang Lai, Chong-Yu Xu. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model[J]. International Journal of Disaster Risk Science, 2023, 14(2): 253-268. doi: 10.1007/s13753-023-00481-2

A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

doi: 10.1007/s13753-023-00481-2

This research acquired financial or data support of the National Key R&D Program of China (2021YFC3001000), the National Natural Science Foundation of China (U1911204, 51879107), the Natural Science Foundation of Guangdong Province (2023B1515020087, 2022A1515010019), and the Fund of Science and Technology Program of Guangzhou (202102020216).

  • Accepted Date: 2023-03-13
  • Available Online: 2023-04-28
  • Publish Date: 2023-04-11
  • Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning.
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  • [1]
    Adhaityar, B.Y., D.P. Sahara, C. Pratama, A. Wibowo, and L.S. Heliani. 2021. Multi-target regression using Convolutional Neural Network-Random Forests (CNN-RF) for early earthquake warning system. Paper presented at the 9th International Conference on Information and Communication Technology (ICoICT), 3-5 August 2021, Yogyakarta, Indonesia.
    Bentivoglio, R., E. Isufi, S.N. Jonkman, and R. Taormina. 2022. Deep learning methods for flood mapping:A review of existing applications and future research directions. Hydrology and Earth System Sciences 26(16):4345-4378.
    Berkhahn, S., L. Fuchs, and I. Neuweiler. 2019. An ensemble neural network model for real-time prediction of urban floods. Journal of Hydrology 575:743-754.
    Bermúdez, M., L. Cea, and J. Puertas. 2019. A rapid flood inundation model for hazard mapping based on least squares support vector machine regression. Journal of Flood Risk Management 12(1):1-14.
    Bhola, P.K., J. Leandro, and M. Disse. 2018. Framework for offline flood inundation forecasts for two-dimensional hydrodynamic models. Geosciences 8(9):Article 346.
    Borchani, H., G. Varando, C. Bielza, and P. Larrañaga. 2015. A survey on multi-output regression. WIREs Data Mining and Knowledge Discovery 5(5):216-233.
    Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.
    Brunton, S.L., B.R. Noack, and P. Koumoutsakos. 2019. Machine learning for fluid mechanics. Annual Review of Fluid Mechanics 52:477-508.
    Chen, W., G. Huang, and H. Zhang. 2017. Urban stormwater inundation simulation based on SWMM and diffusive overland-flow model. Water Science & Technology 76(12):3392-3403.
    Chen, W., G. Huang, H. Zhang, and W. Wang. 2018. Urban inundation response to rainstorm patterns with a coupled hydrodynamic model:A case study in Haidian Island, China. Journal of Hydrology 564:1022-1035.
    Chu, H., W. Wu, Q.J. Wang, R. Nathan, and J. Wei. 2020. An ANN-based emulation modelling framework for flood inundation modelling:Application, challenges and future directions. Environmental Modelling & Software 124:Article 104587.
    Coulthard, T.J., J.C. Neal, P.D. Bates, J. Ramirez, G.A.M. de Almeida, and G.R. Hancock. 2013. Integrating the LISFLOOD-FP 2D hydrodynamic model with the CAESAR model:Implications for modelling landscape evolution. Earth Surface Processes and Landforms 38(15):1897-1906.
    De'ath, G. 2002. Multivariate regression trees:A new technique for modeling species-environment relationships. Ecology 83(4):1105-1117.
    Deng, Z., Z. Wang, X. Wu, C. Lai, and Z. Zeng. 2022. Strengthened tropical cyclones and higher flood risk under compound effect of climate change and urbanization across China's Greater Bay Area. Urban Climate 44:Article 101224.
    Dottori, F., and E. Todini. 2011. Developments of a flood inundation model based on the cellular automata approach:Testing different methods to improve model performance. Physics and Chemistry of the Earth, Parts A/B/C 36(7):266-280.
    Guidolin, M., A.S. Chen, B. Ghimire, E.C. Keedwell, S. Djordjević, and D.A. Savić. 2016. A weighted cellular automata 2D inundation model for rapid flood analysis. Environmental Modelling & Software 84:378-394.
    He, S., Z. Wang, D. Wang, W. Liao, X. Wu, and C. Lai. 2022. Spatiotemporal variability of event-based rainstorm:The perspective of rainfall pattern and concentration. International Journal of Climatology 42(12):6258-6276.
    Hou, J., N. Zhou, G. Chen, M. Huang, and G. Bai. 2021. Rapid forecasting of urban flood inundation using multiple machine learning models. Natural Hazards 108(2):2335-2356.
    IPCC (Intergovernmental Panel on Climate Change). 2021. Climate change 2021:The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, ed. V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, et al. Cambridge, UK and New York, USA:Cambridge University Press.
    Jhong, B.-C., J.-H. Wang, and G.-F. Lin. 2017. An integrated two-stage support vector machine approach to forecast inundation maps during typhoons. Journal of Hydrology 547:236-252.
    Jian, W., S. Li, C. Lai, Z. Wang, X. Cheng, E.Y.-M. Lo, and T.-C. Pan. 2021. Evaluating pluvial flood hazard for highly urbanised cities:A case study of the Pearl River Delta Region in China. Natural Hazards 105(2):1691-1719.
    Kabir, S., S. Patidar, X. Xia, Q. Liang, J. Neal, and G. Pender. 2020. A deep convolutional neural network model for rapid prediction of fluvial flood inundation. Journal of Hydrology 590:Article 125481.
    Keifer, C.J., and H.H. Chu. 1957. Synthetic storm pattern for drainage design. Journal of the Hydraulics Division 83(4):1332-1-1332-25.
    Kim, J., and H. Cho. 2019. Scenario-based urban flood forecast with flood inundation map. Tropical Cyclone Research and Review 8(1):27-34.
    Kocev, D., C. Vens, J. Struyf, and S. Džeroski. 2007. Ensembles of multi-objective decision trees. In Machine learning:ECML 2007, ed. J.N. Kok, J. Koronacki, R.L. de Mantaras, S. Matwin, D. Mladenič, and A. Skowron, 624-631. Berlin and Heidelberg:Springer.
    Kocev, D., S. Džeroski, M.D. White, G.R. Newell, and P. Griffioen. 2009. Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition. Ecological Modelling 220(8):1159-1168.
    Lai, C., Q. Shao, X. Chen, Z. Wang, X. Zhou, B. Yang, and L. Zhang. 2016. Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology 542:268-280.
    Lai, C., X. Chen, Z. Wang, H. Yu, and X. Bai. 2020. Flood risk assessment and regionalization from past and future perspectives at basin scale. Risk Analysis 40(7):1399-1417.
    Li, S., Z. Wang, C. Lai, and G. Lin. 2020. Quantitative assessment of the relative impacts of climate change and human activity on flood susceptibility based on a cloud model. Journal of Hydrology 588:Article 125051.
    Li, S., Z. Wang, X. Wu, Z. Zeng, P. Shen, and C. Lai. 2022. A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC. Journal of Environmental Management 307:Article 114574.
    Liao, Y., Z. Wang, J. Xiong, and C. Lai. 2021. Dimming in the Pearl River Delta of China and the major influencing factors. Climate Research 82:161-176.
    Liaw, A., and M. Wiener. 2002. Classification and regression by randomForest. R News 2(3):18-22.
    Lin, G.-F., H.-Y. Lin, and Y.-C. Chou. 2013. Development of a real-time regional-inundation forecasting model for the inundation warning system. Journal of Hydroinformatics 15(4):1391-1407.
    Lin, Q., J. Leandro, S. Gerber, and M. Disse. 2020. Multistep flood inundation forecasts with resilient backpropagation neural networks:Kulmbach case study. Water 12(12):Article 3568.
    Lin, Y., D. Wang, G. Wang, J. Qiu, K. Long, Y. Du, H. Xie, Z. Wei, et al. 2021. A hybrid deep learning algorithm and its application to streamflow prediction. Journal of Hydrology 601:Article 126636.
    Ling, F., J.-J. Luo, Y. Li, T. Tang, L. Bai, W. Ouyang, and T. Yamagata. 2022. Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole. Nature Communications 13(1):Article 7681.
    Liu, W., D. Xu, I.W. Tsang, and W. Zhang. 2019. Metric learning for multi-output tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(2):408-422.
    McGrath, H., J.-F. Bourgon, J.-S. Proulx-Bourque, M. Nastev, and A.A.E. Ezz. 2018. A comparison of simplified conceptual models for rapid web-based flood inundation mapping. Natural Hazards 93(2):905-920.
    Molokov, M.B., and ΓΓ Shtigorin. 1956. The rain water and confluent channel. Beijing:Architectural Engineering Press (in Chinese).
    Nakano, F.K., K. Pliakos, and C. Vens. 2022. Deep tree-ensembles for multi-output prediction. Pattern Recognition 121:Article 108211.
    Nash, J.E., and J.V. Sutcliffe. 1970. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology 10(3):282-290.
    Pan, C., X. Wang, L. Liu, H. Huang, and D. Wang. 2017. Improvement to the Huff Curve for design storms and urban flooding simulations in Guangzhou, China. Water 9(6):Article 411.
    Rossman, L.A. 2015. Storm water management model user's manual version 5.1. Washington, DC:United States Environmental Protection Agency (USEPA).
    Saha, D., P. Alluri, and A. Gan. 2016. A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection:Random forests to prioritize HSM variables. Journal of Advanced Transportation 50(4):522-540.
    Schulz, A., J. Kiesel, H. Kling, M. Preishuber, and G. Petersen. 2015. An online system for rapid and simultaneous flood mapping scenario simulations-The Zambezi FloodDSS. In Proceedings of EGU General Assembly 2015, 12-17 April 2015, Vienna, Austria.
    Shih, Y.-S., and H.-W. Tsai. 2004. Variable selection bias in regression trees with constant fits. Computational Statistics & Data Analysis 45(3):595-607.
    Struyf, J., and S. Džeroski. 2006. Constraint based induction of multi-objective regression trees. In Knowledge discovery in inductive databases, ed. F. Bonchi, and J.-F. Boulicaut, 222-233. Berlin and Heidelberg:Springer.
    Tellman, B., J.A. Sullivan, C. Kuhn, A.J. Kettner, C.S. Doyle, G.R. Brakenridge, T.A. Erickson, and D.A. Slayback. 2021. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596(7870):80-86.
    Teng, J., J. Vaze, S. Kim, D. Dutta, A.J. Jakeman, and B.F.W. Croke. 2019. Enhancing the capability of a simple, computationally efficient, conceptual flood inundation model in hydrologically complex terrain. Water Resources Management 33(2):831-845.
    Wang, Z., C. Lai, X. Chen, B. Yang, S. Zhao, and X. Bai. 2015. Flood hazard risk assessment model based on random forest. Journal of Hydrology 527:1130-1141.
    Wang, W., W. Chen, and G. Huang. 2021. Urban stormwater modeling with local inertial approximation form of shallow water equations:A comparative study. International Journal of Disaster Risk Science 12(5):745-763.
    Wang, Z., S. Li, X. Wu, G. Lin, and C. Lai. 2022. Impact of spatial discretization resolution on the hydrological performance of layout optimization of LID practices. Journal of Hydrology 612:Article 128113.
    Wu, X., Z. Wang, S. Guo, W. Liao, Z. Zeng, and X. Chen. 2017. Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework:A case study in Dongguan City, China. Journal of Hydrology 547:428-442.
    Wu, X., Z. Wang, S. Guo, C. Lai, and X. Chen. 2018. A simplified approach for flood modeling in urban environments. Hydrology Research 49(6):1804-1816.
    Xiong, J., Z. Wang, C. Lai, Y. Liao, and X. Wu. 2020. Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959-2017. International Journal of Climatology 40(15):6282-6300.
    Xu, D., Y. Shi, I.W. Tsang, Y.-S. Ong, C. Gong, and X. Shen. 2020. Survey on multi-output learning. IEEE Transaction on Neural Networks and Learning Systems 31(7):2409-2429.
    Xu, T., and F. Liang. 2021. Machine learning for hydrologic sciences:An introductory overview. Wiley Interdisciplinary Reviews-Water 8(5):Article e1533.
    Xue, L., Y. Liu, Y. Xiong, Y. Liu, X. Cui, and G. Lei. 2021. A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science & Engineering 196:Article 107801.
    Zeng, Z., Z. Wang, and C. Lai. 2022. Simulation performance evaluation and uncertainty analysis on a coupled inundation model combining SWMM and WCA2D. International Journal of Disaster Risk Science 13(4):448-464.
    Zhang, M., M. Xu, Z. Wang, and C. Lai. 2021. Assessment of the vulnerability of road networks to urban waterlogging based on a coupled hydrodynamic model. Journal of Hydrology 603:Article 127105.
    Zounemat-Kermani, M., O. Batelaan, M. Fadaee, and R. Hinkelmann. 2021. Ensemble machine learning paradigms in hydrology:A review. Journal of Hydrology 598:Article 126266.
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