Rapid urban inundation prediction method based on numerical simulation and AI algorithm

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL Journal of Hydrology Pub Date : 2024-11-17 DOI:10.1016/j.jhydrol.2024.132334
Xinxin Pan , Jingming Hou , Guangzhao Chen , Donglai Li , Nie Zhou , Muhammad Imran , Xinyi Li , Juan Qiao , Xujun Gao
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Abstract

Urban inundation caused by extreme torrential rains has become one of the most prominent natural disasters globally, and rapid and precise forecasting of such events is now a primary measure in flood emergency management. However, AI-based rapid inundation forecasting requires sufficient historical inundation data, and existing forecasts only predict urban inundation without addressing elements such as the load on urban drainage systems. Therefore, this paper combines physical process models and AI technology to develop a rapid forecasting model for urban inundation, designed to quickly predict surface water accumulation, link capacity, and water depth at control nodes in storage pools due to extreme rainfall. To address the issue of insufficient historical rainfall and inundation monitoring data, the model integrates one-dimensional link network models and two-dimensional hydrodynamic models to address the shortage of flood data. The model simulates flood data for various rainfall intensities and patterns in the study area, forming a rainfall-inundation outcome matrix. This matrix is then trained using a BP neural network algorithm, ultimately producing a rapid forecasting model for urban inundation applicable to the study area. The results show: (1) In terms of computational accuracy, the predicted values for surface water accumulation, link capacity, and water depth at storage pool control nodes have R2 values of no less than 0.826, 0.951, and 0.765, respectively, demonstrating the model’s reliable prediction accuracy; (2) In terms of computational efficiency, the rapid forecasting model averages 27.44 s to forecast a single flood event, achieving a speed increase of approximately 322 times compared to traditional two-dimensional hydrodynamic models, indicating a fast computation speed. Thus, this forecasting model can provide more time for urban emergency decision-making, thereby reducing the economic losses and casualties caused by urban inundation.
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基于数值模拟和人工智能算法的城市快速淹没预测方法
特大暴雨造成的城市内涝已成为全球最突出的自然灾害之一,对此类事件进行快速、精确的预测已成为洪水应急管理的首要措施。然而,基于人工智能的快速淹没预报需要足够的历史淹没数据,而现有的预报只预测城市淹没,没有考虑城市排水系统的负荷等因素。因此,本文将物理过程模型与人工智能技术相结合,开发了一种城市内涝快速预测模型,旨在快速预测极端降雨导致的地表积水、链接能力以及蓄水池控制节点的水深。针对历史降雨量和淹没监测数据不足的问题,该模型整合了一维链接网络模型和二维水动力模型,以解决洪水数据不足的问题。该模型模拟研究区域内各种降雨强度和模式的洪水数据,形成降雨-淹没结果矩阵。然后使用 BP 神经网络算法对该矩阵进行训练,最终生成适用于研究区域的城市洪水快速预报模型。结果表明:(1) 在计算精度方面,蓄水池控制节点的地表积水预测值、链接容量预测值和水深预测值的 R2 值分别不小于 0.826、0.951 和 0.765。765,表明该模型的预测精度可靠;(2)在计算效率方面,快速预报模型平均预报一次洪水事件的时间为 27.44s,与传统的二维水动力模型相比,速度提高了约 322 倍,表明计算速度较快。因此,该预报模型可为城市应急决策提供更多时间,从而减少城市淹没造成的经济损失和人员伤亡。
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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