气候变化领域集成服务门户(CCPortal): Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front

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DOI	10.1029/2020GL087857
	Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front
	Obelcz J.; Wood W.T.; Phrampus B.J.; Lee T.R.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:10
英文摘要	The subaqueous Mississippi River Delta Front is prone to seabed instabilities >1 m of vertical bathymetric change per year, but the ability to predict the location and magnitude of instability-driven depth change is limited. Here we demonstrate that data-driven geospatial models can predict MRDF depth change from a small amount (1% of full coverage) of training data. We predict depth change at 100 m2 resolution between 2005 and 2017 over a ~100 km2 area. Models trained on ~1% of full-coverage depth change data produce comparable and relatively low average predicted depth change errors (1–2 cm). K-nearest neighbors best reproduce the spatial variability of depth change and can interpolate and extrapolate from training data. This approach has immediate applications for geohazard monitoring on the MRDF and other geologically similar settings and can be applied in other settings if the drivers of depth change variance are well known. ©2020. The Authors.
英文关键词	Forecasting; Hydrographic surveys; Machine learning; Nearest neighbor search; Bathymetric survey; Data driven; Depth changes; Geospatial model; K-nearest neighbors; Mississippi river; Spatial variability; Training data; Bathymetry; bathymetric survey; environmental monitoring; machine learning; marine environment; seafloor; spatial variation; vertical distribution; Louisiana; Mississippi Delta; United States
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170364
作者单位	U.S. Naval Research Lab, Stennis Space CenterMS, United States
推荐引用方式 GB/T 7714	Obelcz J.,Wood W.T.,Phrampus B.J.,et al. Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front[J],2020,47(10).
APA	Obelcz J.,Wood W.T.,Phrampus B.J.,&Lee T.R..(2020).Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front.Geophysical Research Letters,47(10).
MLA	Obelcz J.,et al."Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front".Geophysical Research Letters 47.10(2020).