A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network
文献类型：期刊论文
| 作者 | Yan, Jiabao; Jia, Shaofeng |
| 刊名 | WATER SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2022-12-05 |
| 页码 | 24 |
| 关键词 | aggregated data artificial neural network-based indirect model fine-resolution global gridded municipal water withdrawal |
| ISSN号 | 0273-1223 |
| DOI | 10.2166/wst.2022.399 |
| 通讯作者 | Jia, Shaofeng(jiasf@igsnrr.ac.cn) |
| 英文摘要 | Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, 6 different models are constructed and evaluated in estimating global MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NNM) shows the best accuracy with higher R-2 and lower NMAE and NRMSE in different spatial scales. The estimates achieved from NNM model are consistent with census and survey data, outperforms the existing global gridded MWW dataset. At last, the NNM model is applied to mapping global gridded MWW for the year 2015 at 0.1 degrees x 0.1 degrees resolution. The proposed method can be applied to a wider aggregated output learning problem and the high-resolution global gridded MWW data can be used in hydrological models and water resources management. |
| WOS关键词 | SATELLITE ; RESOURCES ; MODELS ; TRENDS ; REGION |
| 资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; [XDA20010201] ; [41901047] |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000894035400001 |
| 出版者 | IWA PUBLISHING |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187856]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Jia, Shaofeng |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Jiabao,Jia, Shaofeng. A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network[J]. WATER SCIENCE AND TECHNOLOGY,2022:24. |
| APA | Yan, Jiabao,&Jia, Shaofeng.(2022).A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network.WATER SCIENCE AND TECHNOLOGY,24. |
| MLA | Yan, Jiabao,et al."A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network".WATER SCIENCE AND TECHNOLOGY (2022):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
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