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首页> 外文期刊>Journal of Hydroinformatics >Flood inundation forecasts using validation data generated with the assistance of computer vision
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Flood inundation forecasts using validation data generated with the assistance of computer vision

机译：使用借助计算机视觉生成的验证数据来预测洪水泛滥

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Forecasting flood inundation in urban areas is challenging due to the lack of validation data. Recent developments have led to new genres of data sources, such as images and videos from smartphones and CCTV cameras. If the reference dimensions of objects, such as bridges or buildings, in images are known, the images can be used to estimate water levels using computer vision algorithms. Such algorithms employ deep learning and edge detection techniques to identify the water surface in an image, which can be used as additional validation data for forecasting inundation. In this study, a methodology is presented for flood inundation forecasting that integrates validation data generated with the assistance of computer vision. Six equifinal models are run simultaneously, one of which is selected for forecasting based on a goodness-of-fit (least error), estimated using the validation data. Collection and processing of images is done offline on a regular basis or following a flood event. The results show that the accuracy of inundation forecasting can be improved significantly using additional validation data.
机译：由于缺乏验证数据，预测城市地区的洪水泛滥具有挑战性。最近的发展导致了新的数据源类型，例如来自智能手机和CCTV摄像机的图像和视频。如果图像中物体（例如桥梁或建筑物）的参考尺寸已知，则可以使用计算机视觉算法将图像用于估计水位。此类算法采用深度学习和边缘检测技术来识别图像中的水面，可用作预测淹没的附加验证数据。在这项研究中，提出了一种洪水泛滥预测的方法，该方法整合了借助计算机视觉生成的验证数据。同时运行六个均等模型，其中一个基于拟合优度（最小误差）进行选择，以使用验证数据进行估算，以进行拟合。图像的收集和处理是定期或在发生洪灾后离线进行的。结果表明，使用其他验证数据可以显着提高淹没预报的准确性。

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