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.
时间: 2023-03-06 21:32:08 浏览: 112
回答:本文提出了一种基于底部类型自适应的卫星深度测量(BA-SDB)方法,以解决传统卫星深度测量模型中假设底部均匀的局限性以及不能充分考虑影响反射率的干扰因素,如浊度等。通过基于聚类的底部反射率自适应分割,并采用PSO-LightGBM算法考虑多种因素,实现了对不同沉积物和水深的准确深度估算,与空中激光测深和多波束回声深度声纳联合实测结果相比,均方根误差值达到0.85m,是传统比值法、多波段法和机器学习方法中最准确的方法。
相关问题
mod business object failed Error: #1500096: The revision of a business object cannot be changed without revising the business object or changing the business object name
这个错误提示说明在修改对象版本时,你不能直接修改版本号而不修改对象本身。有两种解决方法:
1. 将对象复制一份并修改其版本号:你可以使用以下命令将对象复制一份,并将其版本号修改为新的版本号:
copy bus <object_type> <object_name> <new_object_name> revision <new_version_id>;
其中,<new_object_name>表示新对象的名称,<new_version_id>表示新的版本号。
然后,你可以使用以下命令将新对象作为原对象的新版本提交:
revise bus <object_type> <object_name> to <new_object_name> <new_version_id>;
2. 修改对象名称并修改版本号:你可以使用以下命令将对象的名称修改为新的名称,并将其版本号修改为新的版本号:
modify bus <object_type> <object_name> name <new_object_name> revision <new_version_id>;
然后,你需要使用以下命令提交修改后的对象:
save <object_type> <new_object_name>;
请注意,这种方法可能会影响到其他引用该对象的地方,因此需要谨慎操作。
无论你选择哪种方法,都需要注意在修改对象版本号时,要遵守ENOVIA的规则和限制。
request for member ‘shang’ in ‘i’, which is of non-class type ‘int’ ^~~~~
As an AI language model, I cannot understand the full context of the issue. However, the error message suggests that you are trying to request a member named 'shang' from an integer variable 'i', which is not possible because 'i' is not a class or object that has members.
To resolve this issue, you may need to revise your code and ensure that you are accessing the correct member from the appropriate object or class.