DOI: 10.1029/2018GB005992
论文题名: A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon
作者: Lee T.R. ; Wood W.T. ; Phrampus B.J.
刊名: Global Biogeochemical Cycles
ISSN: 0886-6236
EISSN: 1944-9224
出版年: 2019
卷: 33, 期: 1 语种: 英语
英文关键词: global perspective
; interpolation
; machine learning
; nearest neighbor analysis
; prediction
; seafloor
; total organic carbon
学科: global prediction
; interpolation techniques
; machine learning
; seafloor properties
; total organic carbon
中文摘要: 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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160166
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
Lee T.R.,Wood W.T.,Phrampus B.J.. A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon[J]. Global Biogeochemical Cycles,2019-01-01,33(1)