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Comparison model learning methods for methane emission prediction of reservoirs on a regional field scale: Performance and adaptation of methods with different experimental datasets

文献类型：期刊论文

作者Li, Gang1,3; Yang, Meng1,2; Zhang, Yunmo4; Grace, John5; Lu, Cai1; Zeng, Qing1; Jia, Yifei1; Liu, Yunzhu1; Lei, Jialin1; Geng, Xuemeng1,6
刊名ECOLOGICAL ENGINEERING
出版日期2020-10-01
卷号157页码:9
关键词Machine Statistical learning methods Model adaptation Methane Reservoir IPCC
ISSN号0925-8574
DOI10.1016/j.ecoleng.2020.105990
通讯作者Lei, Guangchun(guangchun.lei@foxmail.com) ; Chen, Ying(chenying@cau.edu.cn)
英文摘要In this study, we explored different methods to build methane emissions prediction models of temperate reservoirs on a regional field scale, and then we examined the performances and adaptation of these prediction models. First, four statistical model learning methods and two machine learning methods were used to develop methane emissions prediction models based on environmental factors (i.e., temperature and atmospheric pressure) and methane fluxes at three reservoirs (Miyun, Yudushan, and Baihepu) in Beijing, China, from 2009 to 2012. In general, decision trees (DT) exhibited better performance with higher r(2) (the coefficient of determination) and lower root mean squared error, mean deviation, mean squared error, and mean absolute error. Second, in order to examine model adaptation, two experimental datasets were used to build methane emissions prediction models separately: D1 (samples only from Miyun) and D2 (samples from Miyun, Yudushan, and Baihepu). Then, three test data groups which used samples from the three reservoirs separately were used. In general, decision trees (DT) exhibited better performance and adaptation compared to those of other model learning methods. Moreover, our study indicated that it could be necessary to compare greenhouse gas prediction models when compiling greenhouse gas inventories according to IPCC.
WOS关键词GREENHOUSE-GAS EMISSIONS ; FRESH-WATER ; DECISION TREE ; FLUXES ; TEMPERATURE ; REGRESSION ; DIFFUSION ; PONDS ; LAKES
资助项目Anhui Jianzhu University[2019QDZ58/K1930117] ; Anhui province[SK22015A720]
WOS研究方向Environmental Sciences & Ecology ; Engineering
语种英语
WOS记录号WOS:000573277800013
出版者ELSEVIER
资助机构Anhui Jianzhu University ; Anhui province
源URL[http://ir.igsnrr.ac.cn/handle/311030/157023]  
专题中国科学院地理科学与资源研究所
通讯作者Lei, Guangchun; Chen, Ying
作者单位1.Beijing Forestry Univ, Nat Conservat, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Anhui Jianzhu Univ, Sch Architecture & Urban Planning, Hefei 230022, Peoples R China
4.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
5.Univ Edinburgh, Sch Geosci, Edinburgh EH9 3FF, Midlothian, Scotland
6.Beijing Shoufa Tianren Ecol Landscape CO Ltd, Beijing 102600, Peoples R China
推荐引用方式
GB/T 7714		 Li, Gang,Yang, Meng,Zhang, Yunmo,et al. Comparison model learning methods for methane emission prediction of reservoirs on a regional field scale: Performance and adaptation of methods with different experimental datasets[J]. ECOLOGICAL ENGINEERING,2020,157:9.
APA Li, Gang.,Yang, Meng.,Zhang, Yunmo.,Grace, John.,Lu, Cai.,...&Chen, Ying.(2020).Comparison model learning methods for methane emission prediction of reservoirs on a regional field scale: Performance and adaptation of methods with different experimental datasets.ECOLOGICAL ENGINEERING,157,9.
MLA Li, Gang,et al."Comparison model learning methods for methane emission prediction of reservoirs on a regional field scale: Performance and adaptation of methods with different experimental datasets".ECOLOGICAL ENGINEERING 157(2020):9.

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来源：地理科学与资源研究所

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