Abstract
Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (KNN) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods.
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References
Berardi L, Laucelli D, Simeone V et al (2013) Simulating floods in ephemeral streams in Southern Italy by full-2D hydraulic models. Int J River Basin Manag 11(1):1–17. https://doi.org/10.1080/15715124.2012.746975
He BS, Ma MH, Li Q et al (2017) Summary and analysis of my country's mountain flood disaster prevention in 2016. China Flood Control and Drought Relief (01):95–98. https://doi.org/10.16867/j.cnki.cfdm.2017.01.016
Hou JM, Liang QH, Zhang HB et al (2015) An efficient unstructured MUSCL scheme for solving the 2D shallow water equations. Environ Model Softw 66:131–152. https://doi.org/10.1016/j.envsoft.2014.12.007
Hou JM, Wang JH, Tong Y et al (2021a) High-efficiency and high-precision flood process simulation based on non-uniform grid. Eng Sci Technol 53(04):53–62. https://doi.org/10.15961/j.jsuese.202000760
Hou JM, Zhang ZA, Zhang DW et al (2021b) Study on the influence of infiltration on flood propagation with different peak shape coefficients and duration. Water Policy 23:1059–1074. https://doi.org/10.2166/wp.2021.193
Hu P, Han JJ, Lei YL (2019) Coupled modeling of sediment-laden flows based on local-time-step approch. J Zhejiang Univ Eng Sci 53(04):743–752. https://doi.org/10.3785/j.issn.1008-973X.2019.04.015
Jhong YD, Lin HP, Chen CS et al (2022) Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan. Water Resour Manage 36:3221–3245. https://doi.org/10.1007/s11269-022-03197-y
Ji ZH, Wu XH (2018) A Review about the risk assessment of torrential flood disaster. Disaster Sci 33(01):162–167+174.480. https://doi.org/10.3969/j.issn.1000-811X.2018.01.029
Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE 548 transactions on systems, man, and cybernetics (4):580–585. 549. https://doi.org/10.1109/TSMC.1985.6313426550
Khatri S, Kokane P, Kumar V et al (2022) Prediction of waterlogged zones under heavy rainfall conditions using machine learning and GIS tools: a case study of Mumbai. Geojournal 1–15. https://doi.org/10.1007/s10708-022-10731-3
Liu CJ, Wen L, Zhou J et al (2019) Comparison and analysis of flood model and hydrodynamic method calculation of rainstorm mountain in small watershed. Journal of China Institute of Water Resources and Hydropower Research 17(04):262–270+278. https://doi.org/10.13244/j.cnki.jiwhr.2019.04.003
Liu K, Li Z, Yao C et al (2016) Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting. Int J Sedim Res 31(2):149–158. https://doi.org/10.1016/j.ijsrc.2016.02.002
Mekanik F, Imteaz MA, Shirley GT et al (2013) Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503(503):11–21. https://doi.org/10.1016/j.jhydrol.2013.08.035
Montanari A, Young G, Savenije HHG et al (2013) “Panta Rhei—Everything Flows”: change in hydrology and society—the iahs scientific decade 2013–2022. Hydrol Sci J 58(6):1256–1275. https://doi.org/10.1080/02626667.2013.809088
Naghibi SA, Vafakhah M, Hashemi H et al (2020) Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Nat Resour Res 29(3):1915–1933. https://doi.org/10.1007/s11053-019-09530-4
Pérez-Alarcón A, Garcia-Cortes D, Fernández-Alvarez JC et al (2022) Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models. Environ Process 9:53. https://doi.org/10.1007/s40710-022-00602-x
Shahabi H, Shirzadi A, Ghaderi K et al (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12(2). https://doi.org/10.3390/rs12020266
Shao YM, Shao DN (2014) The new generation of rainstorm intensity formula in Chinese cities. China Architecture & Building Press, Beijing
Sivakumar P, Hyams DG, Taylor LK et al (2009) A primitive-variable Riemann method for solution of the shallow water equations with wetting and drying. J Comput Phys 228(19):7452–7472. https://doi.org/10.1016/j.jcp.2009.07.002
Vialetto G, Noro M (2019) Enhancement of a Short-Term Forecasting Method Based on Clustering and KNN: Application to an Industrial Facility Powered by a Cogenerator. Energies 12(23):4407. https://doi.org/10.3390/en12234407
Wu J, Wang Z, Hu Y et al (2023) Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory. Water Resour Manage 37:937–953. https://doi.org/10.1007/s11269-022-03414-8
Zhang DW, Quan J, Ma JM et al (2018) Two-dimensional numerical simulation of watershed surface runoff based on Godunov format. J Hydraul Eng 49(07):787–794+802. https://doi.org/10.13243/j.cnki.slxb.20180388
Funding
This work is partly supported by the Numerical Simulation of Flood Process in Urban Areas with Fine Terrain and Lack of Pipe Network Data (No. 52079106); High Resolution Numerical Simulation of Sediment Carrying Capacity Mechanism and Erosion Process of Over surface Flow on Whole Sand Slope (No. 52009104); Chinesisch-Deutsches Mobilit taprogramm: High – Resolution Numerical Simulating and Predicting Methods for Urban Floods (No. M-0427).
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Conceptualization and Methodology: N. Zhou, JM. Hou; Writing-original draft preparation: N. Zhou; Material preparation, collection and analysis: N. Zhou, H. Chen, GZ. Chen; Supervision: BY. Liu; Funding acquisition: JM. Hou.
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Zhou, N., Hou, J., Chen, H. et al. A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm. Water Resour Manage 38, 1903–1919 (2024). https://doi.org/10.1007/s11269-023-03664-0
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DOI: https://doi.org/10.1007/s11269-023-03664-0