MIKE21 Bathymetry前处理指南

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"这篇内容主要涉及MIKE21软件的数据前处理部分,特别是关于Bathymetry的设定和Time SeriesEditor的使用。MIKE21是一款强大的水动力学模拟软件,而前处理阶段是构建模型并准备初始数据的关键步骤。在设定Bathymetry时,用户需要通过Bathymetry Management来创建新的计算域,指定网格参数如原点位置、网格尺寸、节点数、偏转角度以及陆地值,并可以选择是否显示边界和节点。此外,Time SeriesEditor则是用来创建和编辑0型数据文件,这些文件通常包含时间轴上的特定条目数据,可以作为模型输入或后处理结果。这个编辑器支持新建TS文件、设置文件属性、使用计算器进行数值计算、执行插值处理填补缺失数据,以及调整图形显示的视觉效果。" 在MIKE21的前处理过程中,Bathymetry的设定至关重要,因为它定义了水体底部的地形特征,直接影响到水流模拟的准确性。用户可以通过Bathymetry Management模块设定计算域的基本属性,包括计算域的地理定位、网格化细节和方向校正,这对于精确模拟水流动力学行为是必不可少的。 Time SeriesEditor是另一个核心工具,用于处理时间序列数据。用户可以创建新的TS文件,选择不同的数据导入方式,例如从ASCII文本文件导入或基于模板创建。在TS文件中,用户可以定义时间轴的类型(如时间轴或相对条目轴)、时间步长、起始时刻和步数,并且可以添加、删除或修改条目信息。该编辑器还提供了计算器功能,允许对整个条目列或选定的子集执行数值运算。如果数据存在缺失,插值工具可以帮助用户进行线性插值,以填补数据空白。最后,图形显示功能使得用户能够自定义图形的颜色、标记和线型,以便于数据分析和结果呈现。 通过这些工具,用户能够有效地准备MIKE21模型所需的初始条件,确保模拟的准确性和可靠性。在进行水环境模拟时,正确配置Bathymetry和管理时间序列数据是至关重要的步骤,它们共同构成了MIKE21模型的基石。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传