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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
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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). |
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