气候变化领域集成服务门户(CCPortal): A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon

CCPortal

DOI	10.1029/2018GB005992
	A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon
	Lee T.R.; Wood W.T.; Phrampus B.J.
发表日期	2019
ISSN	0886-6236
EISSN	1944-9224
起始页码	37
结束页码	46
卷号	33 期号:1
英文摘要	Seafloor properties, including total organic carbon (TOC), are sparsely measured on a global scale, and interpolation (prediction) techniques are often used as a proxy for observation. Previous geospatial interpolations of seafloor TOC exhibit gaps where little to no observed data exists. In contrast, recent machine learning techniques, relying on geophysical and geochemical properties (e.g., seafloor biomass, porosity, and distance from coast), show promise in making comprehensive, statistically optimal predictions. Here we apply a nonparametric (i.e., data-driven) machine learning algorithm, specifically k-nearest neighbors (kNN), to estimate the global distribution of seafloor TOC. Our results include predictor (feature) selection specifically designed to mitigate bias and produce a statistically optimal estimation of seafloor TOC, with uncertainty, at 5 × 5-arc minute resolution. Analysis of parameter space sample density provides a guide for future sampling. One use for this prediction is to constrain a global inventory, indicating that just the upper 5 cm of the seafloor contains about 87 ± 43 gigatons of carbon (Gt C) in organic form. ©2019. This article is a US Government work and is in the public domain in the USA.
英文关键词	global prediction; interpolation techniques; machine learning; seafloor properties; total organic carbon
语种	英语
scopus关键词	global perspective; interpolation; machine learning; nearest neighbor analysis; prediction; seafloor; total organic carbon
来源期刊	Global Biogeochemical Cycles
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129762
作者单位	U.S. Naval Research Laboratory, John C. Stennis Space Center, Hancock County, MS, United States; ASEE Postdoctoral Program, U.S. Naval Research Laboratory, John C. Stennis Space Center, Hancock County, MS, United States
推荐引用方式 GB/T 7714	Lee T.R.,Wood W.T.,Phrampus B.J.. A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon[J],2019,33(1).
APA	Lee T.R.,Wood W.T.,&Phrampus B.J..(2019).A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon.Global Biogeochemical Cycles,33(1).
MLA	Lee T.R.,et al."A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon".Global Biogeochemical Cycles 33.1(2019).