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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1111/gcb.14845
|2 doi
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|a pubmed24n1005.xml
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|a (DE-627)NLM301615934
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|a (NLM)31553826
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Kim, Yeonuk
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Gap-filling approaches for eddy covariance methane fluxes
|b A comparison of three machine learning algorithms and a traditional method with principal component analysis
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 19.03.2020
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|a Date Revised 19.03.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2019 John Wiley & Sons Ltd.
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|a Methane flux (FCH4 ) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap-filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4 . However, there is still no consensus regarding FCH4 gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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|a artificial neural network
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| 650 |
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4 |
|a comparison of gap-filling techniques
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| 650 |
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|a eddy covariance
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| 650 |
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|a machine learning
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| 650 |
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|a marginal distribution sampling
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| 650 |
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|a methane flux
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| 650 |
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|a random forest
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| 650 |
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|a support vector machine
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| 650 |
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|a Carbon Dioxide
|2 NLM
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| 650 |
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7 |
|a 142M471B3J
|2 NLM
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| 650 |
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7 |
|a Methane
|2 NLM
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| 650 |
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7 |
|a OP0UW79H66
|2 NLM
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| 700 |
1 |
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|a Johnson, Mark S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Knox, Sara H
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Black, T Andrew
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dalmagro, Higo J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kang, Minseok
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kim, Joon
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Baldocchi, Dennis
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 26(2020), 3 vom: 04. März, Seite 1499-1518
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
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| 773 |
1 |
8 |
|g volume:26
|g year:2020
|g number:3
|g day:04
|g month:03
|g pages:1499-1518
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| 856 |
4 |
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|u http://dx.doi.org/10.1111/gcb.14845
|3 Volltext
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|a GBV_USEFLAG_A
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|a SYSFLAG_A
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|a GBV_NLM
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|a GBV_ILN_350
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|a AR
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|d 26
|j 2020
|e 3
|b 04
|c 03
|h 1499-1518
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