论文标题
数据驱动的洪水仿真：通过深层卷积神经网络加快城市洪水预测
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
论文作者
论文摘要
计算复杂性一直是在具有高空间分辨率的大型城市地区应用基于物理模拟的瓶颈，以进行高效和系统的洪水分析和风险评估。为了解决这个漫长的计算时间问题，本文提出，最大水深栅格的预测可以被视为图像到图像翻译问题，其中使用从数据中学到的信息而不是通过进行模拟来从输入升级横向器中产生结果，这可以显着加速预测过程。提出的方法是由深度卷积神经网络实施的，该网络对18个选定集水区设计的18种设计射击图进行了洪水模拟数据。进行了多次设计和实际降雨事件的测试，结果表明，与基于物理的方法相比，神经网络的洪水预测仅使用0.5％的时间，具有有希望的准确性和概括能力。拟议的神经网络还可以可能应用于不同但相关的问题，包括城市布局计划的洪水预测。
Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning.
