Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model

Abstract

Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.

Keywords

China / Flood prediction / Hainan / Hydrological–hydraulic model / Light gradient boosting machine / Urban floods

References

[1] Ahiablame L, Shakya R. Modeling flood reduction effects of low impact development at a watershed scale. Journal of Environmental Management, 2016, 171: 81-91

[2] Aronica GT, Franza F, Bates PD, Neal JC. Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation. Hydrological Processes, 2012, 26(26): 3962-3972

[3] Bates PD, Horritt MS, Fewtrell TJ. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. Journal of Hydrology, 2010, 387(1–2): 33-45

[4] Berkhahn S, Fuchs L, Neuweiler I. An ensemble neural network model for real-time prediction of urban floods. Journal of Hydrology, 2019, 575: 743-754

[5] Bermúdez M, Ntegeka V, Wolfs V, Willems P. Development and comparison of two fast surrogate models for urban pluvial flood simulations. Water Resources Management, 2018, 32(8): 2801-2815

[6] Berz G. Flood disasters: Lessons from the past—worries for the future. Proceedings of the Institution of Civil Engineers-Water and Maritime Engineering, 2000, 142(1): 3-8

[7] 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.

[8] Cui, Z., X. Qing, H. Chai, S. Yang, Y. Zhu, and F. Wang. 2021. Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis. Journal of Hydrology 603: Article 127124.

[9] Dai W, Cai Z. Predicting coastal urban floods using artificial neural network: The case study of Macau China. Applied Water Science, 2021, 11(10): 1-11

[10] Fang J, Liu W, Yang S, Brown S, Nicholls RJ, Hinkel J, Shi X, Shi P. Spatial-temporal changes of coastal and marine disasters risks and impacts in Mainland China. Ocean & Coastal Management, 2017, 139: 125-140

[11] Ferguson BK, Suckling PW. Changing rainfall-runoff relationships in the urbanizing peachtree creek watershed, Atlanta, Georgia. JAWRA Journal of the American Water Resources Association, 1990, 26(2): 313-322

[12] Frank E, Sofia G, Fattorelli S. Mambretti S, di Milano P. Effects of topographic data resolution and spatial model resolution on hydraulic and hydro-morphological models for flood risk assessment. Flood risk assessment and management, 2011, Southampton: WIT Press 23-34

[13] Huang G, Wang X, Huang W. Simulation of rainstorm water logging in urban area based on InfoWorks ICM model. Water Resources and Power, 2017, 35(2): 66-70.

[14] Jain A, Nandakumar K, Ross A. Score normalization in multimodal biometric systems. Pattern Recognition, 2005, 38(12): 2270-2285

[15] 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.

[16] Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017, 30: 3146-3154.

[17] Kim, H.I., and K.Y. Han. 2020. Urban flood prediction using deep neural network with data augmentation. Water 12(3): Article 899.

[18] Lee Y-M, Ko C-M, Shin S-C, Kim B-S. The development of a rainfall correction technique based on machine learning for hydrological applications. Journal of Environmental Science International, 2019, 28(1): 125-135

[19] Li P, Wu Q, Burges C. McRank: Learning to rank using multiple classification and gradient boosting. Advances in Neural Information Processing Systems, 2007, 20: 897-904.

[20] Liang, W., S. Luo, G. Zhao, and H. Wu. 2020. Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics 8(5): Article 765.

[21] Liu C, Yin SQ, Zhang M, Zeng Y, Liu JY. An improved grid search algorithm for parameters optimization on SVM. Applied Mechanics and Materials, 2014, 644: 2216-2219

[22] Liu Y, Zhang S, Liu L, Wang X, Huang H. Research on urban flood simulation: A review from the smart city perspective. Progress in Geography, 2015, 34(4): 494-504.

[23] Löwe, R., J. Böhm, D.G. Jensen, J. Leandro, and S.H. Rasmussen. 2021. U-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth. Journal of Hydrology 603: Article 126898.

[24] Ma, M., G. Zhao, B. He, Q. Li, H. Dong, S. Wang, and Z. Wang. 2021. XGBoost-based method for flash flood risk assessment. Journal of Hydrology 598: Article 126382.

[25] McLachlan GJ, Do K-A, Ambroise C. Analyzing microarray gene expression data, 2004, New York: Wiley

[26] Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 1970, 10(3): 282-290

[27] Nembrini S, König IR, Wright MN. The revival of the Gini importance?. Bioinformatics, 2018, 34(21): 3711-3718

[28] Nguyen Q-H, Nguyen H-D, Le DT, Bui Q-T. Fine-tuning LightGBM using an artificial ecosystem-based optimizer for forest fire analysis. Forest Science, 2022

[29] Ogunleye A, Wang Q-G. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 17(6): 2131-2140

[30] Olbert AI, Comer J, Nash S, Hartnett M. High-resolution multi-scale modelling of coastal flooding due to tides, storm surges and rivers inflows. A Cork City example. Coastal Engineering, 2017, 121: 278-296.

[31] Pontes FJ, Amorim G, Balestrassi PP, Paiva A, Ferreira JR. Design of experiments and focused grid search for neural network parameter optimization. Neurocomputing, 2016, 186: 22-34

[32] Ranka S, Singh V. CLOUDS: A decision tree classifier for large datasets. In Proceedings of the 4th Knowledge Discovery and Data Mining Conference, 1998, 2(8): 2-8.

[33] Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat Deep learning and process understanding for data-driven earth system science. Nature, 2019, 566(7743): 195-204

[34] Sadler JM, Goodall JL, Morsy MM, Spencer K. Modeling urban coastal flood severity from crowd-sourced flood reports using poisson regression and random forest. Journal of Hydrology, 2018, 559: 43-55

[35] Sandri M, Zuccolotto P. A bias correction algorithm for the Gini variable importance measure in classification trees. Journal of Computational and Graphical Statistics, 2008, 17(3): 611-628

[36] Sidek, L.M., A.S. Jaafar, W.H.A.W.A. Majid, H. Basri, M. Marufuzzaman, M.M. Fared, and W.C. Moon. 2021. High-resolution hydrological-hydraulic modeling of urban floods using InfoWorks ICM. Sustainability 13(18): Article 10259.

[37] Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 1974, 36(2): 111-147.

[38] Thorndahl S, Nielsen JE, Jensen DG. Urban pluvial flood prediction: A case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs. Water Science and Technology, 2016, 74(11): 2599-2610

[39] Varoquaux G, Buitinck L, Louppe G, Grisel O, Pedregosa F, Mueller A. Scikit-learn: Machine learning without learning the machinery. GetMobile: Mobile Computing and Communications, 2015, 19(1): 29-33

[40] Wang, Q., P.-H. Wang, and Z.-G. Su. 2013. A hybrid search strategy based particle swarm optimization algorithm. In Proceedings of the 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), 19–21 June 2013, Melbourne, Australia, 301–306.

[41] Wu H, Huang G. Risk assessment of urban waterlogging based on PCSWMM model. Water Resources Protection, 2016, 32(5): 11-16.

[42] Wu X, Wang Z, Guo S, Liao W, Zeng Z, Chen X. Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: A case study in Dongguan City, China. Journal of Hydrology, 2017, 547: 428-442

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

[44] Xu H, Xu K, Lian J, Ma C. Compound effects of rainfall and storm tides on coastal flooding risk. Stochastic Environmental Research and Risk Assessment, 2019, 33(7): 1249-1261

[45] Xu H, Zhang X, Guan X, Wang T, Ma C, Yan D. Amplification of flood risks by the compound effects of precipitation and storm tides under the nonstationary scenario in the coastal city of Haikou, China. International Journal of Disaster Risk Science, 2022, 13(4): 602-620

[46] Yamazaki, D., S. Kanae, H. Kim, and T. Oki. 2011. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resources Research 47(4): Article W04501.

[47] Zanchetta, A.D., and P. Coulibaly. 2020. Recent advances in real-time pluvial flash flood forecasting. Water 12(2): Article 570.

[48] Zevenbergen C, Veerbeek W, Gersonius B, Van Herk S. Challenges in urban flood management: travelling across spatial and temporal scales. Journal of Flood Risk Management, 2008, 1(2): 81-88