Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative mode...Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.展开更多
在暴雨作用下,大型城市极易发生内涝。基于内涝易发性和脆弱性两方面,选取年均降雨量、坡度、高程及人口密度等12项评价指标,将模糊层次分析法(Analytic Hierarchy Process,AHP)与逼近理想解排序法(Technique for Order Preference by S...在暴雨作用下,大型城市极易发生内涝。基于内涝易发性和脆弱性两方面,选取年均降雨量、坡度、高程及人口密度等12项评价指标,将模糊层次分析法(Analytic Hierarchy Process,AHP)与逼近理想解排序法(Technique for Order Preference by Similarity to the Ideal Solution,TOPSIS)进行组合,构建了城市内涝风险评估体系。以长沙市核心城区为例,进行了内涝风险评价,并利用历史内涝点数据进行了验证。内涝易发性评价结果显示,研究区内高和中内涝易发性区域分别占总面积的12.63%和29.52%,主要位于城市中部和东南部,其中芙蓉区有45.50%的区域内涝易发性高,内涝问题最为突出。此外,研究发现195个历史内涝点分别有36.41%位于高内涝易发区,45.13%位于中内涝易发区。暴雨降雨量、高程和坡度是内涝发生的最重要的3个影响因子。在考虑建筑密度、敏感性基础设施与人口密度等脆弱性指标后,研究区内涝高风险区域占总面积的12.2%,主要位于雨花区及岳麓区等人口和建筑密度较大的老城区。内涝风险评估是提升城市韧性的重要内容之一,研究可为城市内涝防灾减灾以及韧性城市的建设提供决策参考。展开更多
针对上海市城区排水系统的水文水力学特性,以SWMM(Storm W aterManagementModel)为基础开发出适合上海市区产流及排水特点和防汛管理要求的城市雨洪模型。模拟结果表明,该模型的计算结果较为理想、可靠,可在实时和规划条件下动态模拟各...针对上海市城区排水系统的水文水力学特性,以SWMM(Storm W aterManagementModel)为基础开发出适合上海市区产流及排水特点和防汛管理要求的城市雨洪模型。模拟结果表明,该模型的计算结果较为理想、可靠,可在实时和规划条件下动态模拟各排水片和街区的地面积水全过程,并能满足市区防汛预报、水情分析、工程规划与管理等工作的要求。该模型对其他城市的类似工作具有较好的参考价值和指导意义。展开更多
基金supported by the General Program of National Natural Science Foundation of China(Grant No.42377467)。
文摘Deep learning models demonstrate impressive performance in rapidly predicting urban floods,but there are still limitations in enhancing physical connectivity and interpretability.This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton(CNN-WCA)to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results.The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP,and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA.Subsequently,the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City.The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP,with the mean absolute error concentrated within 5 mm,and the prediction time of CNN-WCA was only 0.8%that of LISFLOOD-FP.The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding,with the Nash-Sutcliffe e fficiency values of most flood-prone points exceeding 0.97.Furthermore,the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN.The CNN-WCA model with additional physical constraints exhibits a reduction of around 34%in instances of physical discontinuity compared to CNN.Our results prove that the CNN model with multiple physical constraints has signifi cant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.
文摘城市地表特征的正确表达,尤其是地表微地形结构的正确处理是影响内涝模型准确性的主要因素.为了实现高精度的城市内涝模拟,以传统地形测绘数据为基础,对城市地表DEM标准处理方法进行了研究.研究内容包括地表伪洼蓄点的修正处理方法、城市道路地形的处理方法和建筑物高度修正处理方法,并通过实例研究说明了方法应用的流程和效果.研究通过ArcGIS水文分析模块实现了洼地区域的识别和修正;通过不同类型建筑物离地高度的DEM修正,实现了建筑物影响下的周边水流运动过程模拟;通过道路地形的拟合加密,有效解决现有道路测绘高程数据不足的问题,实现了轮廓清晰、路面平滑的道路地形构建.通过案例研究表明,地形处理对模型计算结果将造成较大影响,案例区域不同降雨情景,地形处理前后的积水深度数值偏差达到10.5%~63.3%,深度差值为0.14~0.38 m.
文摘在暴雨作用下,大型城市极易发生内涝。基于内涝易发性和脆弱性两方面,选取年均降雨量、坡度、高程及人口密度等12项评价指标,将模糊层次分析法(Analytic Hierarchy Process,AHP)与逼近理想解排序法(Technique for Order Preference by Similarity to the Ideal Solution,TOPSIS)进行组合,构建了城市内涝风险评估体系。以长沙市核心城区为例,进行了内涝风险评价,并利用历史内涝点数据进行了验证。内涝易发性评价结果显示,研究区内高和中内涝易发性区域分别占总面积的12.63%和29.52%,主要位于城市中部和东南部,其中芙蓉区有45.50%的区域内涝易发性高,内涝问题最为突出。此外,研究发现195个历史内涝点分别有36.41%位于高内涝易发区,45.13%位于中内涝易发区。暴雨降雨量、高程和坡度是内涝发生的最重要的3个影响因子。在考虑建筑密度、敏感性基础设施与人口密度等脆弱性指标后,研究区内涝高风险区域占总面积的12.2%,主要位于雨花区及岳麓区等人口和建筑密度较大的老城区。内涝风险评估是提升城市韧性的重要内容之一,研究可为城市内涝防灾减灾以及韧性城市的建设提供决策参考。
文摘针对上海市城区排水系统的水文水力学特性,以SWMM(Storm W aterManagementModel)为基础开发出适合上海市区产流及排水特点和防汛管理要求的城市雨洪模型。模拟结果表明,该模型的计算结果较为理想、可靠,可在实时和规划条件下动态模拟各排水片和街区的地面积水全过程,并能满足市区防汛预报、水情分析、工程规划与管理等工作的要求。该模型对其他城市的类似工作具有较好的参考价值和指导意义。
