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Merging imagery and models for river current prediction

机译:合并图像和模型以进行河流水流预测

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To meet the challenge of operating in river environments with denied access and to improve the riverine intelligence available to the warfighter, advanced high resolution river circulation models are combined with remote sensing feature extraction algorithms to produce a predictive capability for currents and water levels in rivers where a priori knowledge of the river environment is limited. A River Simulation Tool (RST) is developed to facilitate the rapid configuration of a river model. River geometry is extracted from the automated processing of available imagery while minimal user input is collected to complete the parameter and forcing specifications necessary to configure a river model. Contingencies within the RST accommodate missing data such as a lack of water depth information and allow for ensemble computations. Successful application of the RST to river environments is demonstrated for the Snohomish River, WA. Modeled currents compare favorably to in-situ currents reinforcing the value of the developed approach.
机译:为了应对在无法通行的河流环境中运行的挑战并提高作战人员可用的河流情报,先进的高分辨率河流环流模型与遥感特征提取算法相结合,可为河流中的水流和水位提供预测能力对河流环境的先验知识是有限的。开发了河流模拟工具(RST)以促进快速配置河流模型。从可用图像的自动处理中提取河流几何形状,同时收集最少的用户输入以完成参数并强制配置河流模型所需的规范。 RST内的突发事件可容纳缺少的数据,例如缺乏水深信息,并允许进行整体计算。华盛顿州的Snohomish河展示了RST在河流环境中的成功应用。建模电流与增强已开发方法价值的原位电流相比具有优势。

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