A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations
文献类型：期刊论文
| 作者 | Yan, Pengdong3; Lu, Hongwei4; Chen, Yizhong1; Li, Ziheng5; Li, Hao2 |
| 刊名 | JOURNAL OF CLEANER PRODUCTION
 |
| 出版日期 | 2021-10-15 |
| 卷号 | 319页码:12 |
| ISSN号 | 0959-6526 |
| 关键词 | Water Carbon and ecological footprints Smart evaluation and prediction Ensemble inversion model Urban agglomeration Yangtze river |
| DOI | 10.1016/j.jclepro.2021.128665 |
| 通讯作者 | Lu, Hongwei(luhw@igsnrr.ac.cn) |
| 英文摘要 | Footprint evaluation is an important tool for assessing the appropriation of ecological assets, GHG emissions, freshwater consumption parameters, etc., within a specified region. However, traditional evaluation of footprints for mega cities or urban agglomerations requires overmuch different types of high-quality data. There is a great need of seeking a smart model/approach with declined data requirements for evaluation of footprints where part of data can hardly be accessed. Here we propose a new ensemble inversion model (EIM) based on integrated multitask machine learning (MML) and multi-modeling stacking (MMS) algorithms for smart evaluation and prediction of water, carbon and ecological footprints. The accuracy and generalization capability of the model are illustrated through three largest urban agglomerations in the middle reaches of the Yangtze River (MRYR). The testing results show that the EIM achieves similar prediction performance compared to traditional footprints calculation methods (R-2 = 0.91, RMSE = 0.18, MAE = 0.11), yet greatly reduces the amount of required data by approximately 80%. Moreover, the accuracy of the EIM is improved by more than 20%, compared with other models using a single inversion algorithm. The modeling results also show that 1) water, carbon and ecological footprints are significantly positively correlated, and 2) an annual increase of 4.8% can be found in terms of the urban environmental pressure index (UEPI), and its projection is even less optimistic for the future. |
| WOS关键词 | FOOTPRINT FAMILY ; OPTIMIZATION ; CONSUMPTION ; EMISSIONS ; LAND |
| 资助项目 | National Natural Science Foundation of China[41890824] ; National Key Research and Development Program of China[2019YFC0507801] ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK1003] ; CAS Inter-disciplinary Innovation Team[JCTD-2019-04] |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000728517300004 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; CAS Inter-disciplinary Innovation Team |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/168763]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lu, Hongwei |
| 作者单位 | 1.Hebei Univ Technol, Sch Econ & Management, Tianjin, Peoples R China 2.Cent South Univ, Coll Mech & Elect Engn, Changsha, Peoples R China 3.Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 5.Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Pengdong,Lu, Hongwei,Chen, Yizhong,et al. A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations[J]. JOURNAL OF CLEANER PRODUCTION,2021,319:12. |
| APA | Yan, Pengdong,Lu, Hongwei,Chen, Yizhong,Li, Ziheng,&Li, Hao.(2021).A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations.JOURNAL OF CLEANER PRODUCTION,319,12. |
| MLA | Yan, Pengdong,et al."A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations".JOURNAL OF CLEANER PRODUCTION 319(2021):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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