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《工程(英文)》 >> 2023年 第21卷 第2期 doi: 10.1016/j.eng.2021.11.021

基于图像的深度学习降雨强度估计方法

a College of Civil Engineering and Architecture, Zhejiang University, China
b Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
c KWR Water Research Institute, Nieuwegein 3430 BB, The Netherlands
d Centre for Water Systems, University of Exeter, North Park Road, Exeter, EX4 4QF, United Kingdom
e Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
f Department of Water Management, Delft University of Technology, Delft 2600 GA, The Netherlands

收稿日期: 2021-07-09 修回日期: 2021-09-27 录用日期: 2021-11-15 发布日期: 2022-01-27

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摘要

城市洪水是世界性的重大问题,造成巨大的经济损失,严重威胁公共安全。减轻其影响的一种有希望的方法是开发实时洪水风险管理系统;然而,由于缺乏高时空降雨数据,构建这样一个系统通常具有挑战性。虽然一些方法(即地面降雨站或雷达和卫星技术)可用于测量和(或)预测降雨强度,但使用这些方法很难获得具有理想时空分辨率的准确降雨数据。本文提出了一种基于图像的深度学习模型来估计具有高时空分辨率的城市降雨强度。进一步来说,一种称为基于图像的降雨卷积神经网络(image-based rainfall convolutional neural network, irCNN)模型是使用从现有密集传感器(即智能手机或交通摄像头)收集的降雨图像及其相应的测量降雨强度值开发的。随后使用经过训练的irCNN 模型根据传感器的降雨图像有效地估计降雨强度。分别利用合成降雨数据和真实降雨图像来探索irCNN 在理论和实际模拟降雨强度方面的准确性。结果表明,irCNN 模型提供的降雨量估计值的平均绝对百分比误差在13.5%~21.9%之间,超过了文献中其他最先进的建模技术的性能。更重要的是,所提出的irCNN 的主要特点是它在有效获取高时空城市降雨数据方面成本较低。irCNN 模型为估算城市降雨强度提供了一种有前景的替代方案,可以极大地促进城市实时洪水风险管理的发展。

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