Simulating and mitigating extreme urban heat island effects in a factory area based on machine learning
文献类型：期刊论文
| 作者 | Liu, Shidong1; Zhang, Jianjun1,2; Li, Jiao1; Li, Yuqing1; Zhang, Jie3; Wu, Xia1 |
| 刊名 | BUILDING AND ENVIRONMENT
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 202页码:13 |
| 关键词 | Extreme urban heat island Factory Internal layout Machine learning Marginal utility Thermal environment |
| ISSN号 | 0360-1323 |
| DOI | 10.1016/j.buildenv.2021.108051 |
| 通讯作者 | Zhang, Jianjun(zhangjianjun_bj@126.com) |
| 英文摘要 | Urban heat islands (UHIs) have caused radical changes in urban climates. However, the extreme UHI (E-UHI) formed in factory areas deserves more attention. To mitigate the E-UHI, machine learning is used for simulating and quantifying the marginal utility of the scale, shape, type, stage, and structure of the factory on the land surface temperature (LST), factory LST (LSTf), surrounding LST (LSTs) and increase value (Delta LST) level. The results show that the scale of all types of factories affects LSTf and LSTs, and the shape of steel factories affects LSTs and Delta LST. The LST in factories that require high-temperature environments (e.g., smelters) is significantly higher than that in other factories (e.g., sales plants). The Delta LST of green space (GS), staff activity ground (SG), material transfer ground (MG), material storage area (MA), factory building (FB), smelting area (SA) and casting building (CB) are 3.95 degrees C, 4.01 degrees C, 5.08 degrees C, 5.15 degrees C, 5.24 degrees C, 5.49 degrees C and 7.32 degrees C, and their optimal ranges are 8.84%-15.09%, 16.65%-25.52%, 3.91%-35.91%, 0.00%-8.70%, 5.06%-13.60%, 23.33%-48.02%, and 0.00%- 5.73%, respectively. Appropriately standardizing the scale and shape, controlling the temperature of the hightemperature generation stage, reducing the proportion of CB, MG and MA, and increasing the proportion of GS and SG are effective ways to alleviate the E-UHI. The findings provide theoretical guidance for resource-based cities to mitigate E-UHIs. |
| WOS关键词 | LAND ; IMPACT ; CITY |
| 资助项目 | National Natural Science Foundation of China[41971260] ; Fundamental Research Funds for the Central Universities[2652018033] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000679246000002 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164741]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Jianjun |
| 作者单位 | 1.China Univ Geosci Beijing, Sch Land Sci & Technol, Beijing 100083, Peoples R China 2.Minist Nat Resources, Key Lab Land Consolidat & Rehabil, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Shidong,Zhang, Jianjun,Li, Jiao,et al. Simulating and mitigating extreme urban heat island effects in a factory area based on machine learning[J]. BUILDING AND ENVIRONMENT,2021,202:13. |
| APA | Liu, Shidong,Zhang, Jianjun,Li, Jiao,Li, Yuqing,Zhang, Jie,&Wu, Xia.(2021).Simulating and mitigating extreme urban heat island effects in a factory area based on machine learning.BUILDING AND ENVIRONMENT,202,13. |
| MLA | Liu, Shidong,et al."Simulating and mitigating extreme urban heat island effects in a factory area based on machine learning".BUILDING AND ENVIRONMENT 202(2021):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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