An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China
文献类型：期刊论文
| 作者 | Zhou, Yuanyuan1,2; Li, Xu3; Tang, Qiuhong4,5; Kuok, Sin Chi1,2; Fei, Kai1,2; Gao, Liang1,2 |
| 刊名 | EARTH AND SPACE SCIENCE
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 9期号:4页码:19 |
| DOI | 10.1029/2021EA002043 |
| 通讯作者 | Gao, Liang(gaoliang@um.edu.mo) |
| 英文摘要 | Remote sensing technique is beneficial for rainfall data retrievals, however, enhancing the accuracy remains a challenge. In this study, a novel framework based on a broad learning system (BLS) was proposed to assimilate multi-source data. The dataset includes six satellite-based rainfall products (3B42V7, 3B42RT, IMERG, CBLD, GSMaP, and PCDR), gauge-based rainfall, and environmental data (temperature, specific humidity, wind speed, and locations) from 1 March 2014 to 31 December 2017 over southeast China (SEC). Leave-one-year-out cross-validation (LOYOCV) and independent validation were used to evaluate the BLS assimilating model. The proposed BLS model outperformed six original satellite-based products on Pearson's correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe coefficient of efficiency (NSE) in each test year of LOYOCV. BLS model considering the environmental factors performed better on CC, RMSE, and NSE compared to that without environmental factors. Seasonal variations of daily gauge-based precipitation were accurately captured by BLS-based estimates. BLS method outperformed satellites on CC, RMSE, and NSE at most validation sites at low altitudes (0-1000 m). According to the independent validation, more accurate daily precipitation estimates could be obtained at more than half of the validation sites using the proposed model compared to the source datasets. The BLS-based framework considering environmental factors has the potential to improve estimates over SEC and is expected to be applied to other regions. |
| WOS关键词 | NEURAL-NETWORKS ; RAINFALL ESTIMATION ; PASSIVE MICROWAVE ; HIGH-RESOLUTION ; PRODUCTS ; INTERPOLATION ; REANALYSIS ; ERROR ; IMERG ; TMPA |
| 资助项目 | Science and Technology Development Fund, Macau SAR[SKL-IOTSC-2021-2023] ; Science and Technology Development Fund, Macau SAR[0030/2020/A1] ; Science and Technology Development Fund, Macau SAR[0021/2020/ASC] ; UM Research Grant[SRG2019-00193IOTSC] ; UM Research Grant[MYRG2020-00072-IOTSC] ; Guangdong-Hong Kong-Macau Joint Laboratory Program[2020B1212030009] ; CORE[EF017/IOTSC-GL/2020/HKUST] |
| WOS研究方向 | Astronomy & Astrophysics ; Geology |
| 语种 | 英语 |
| 出版者 | AMER GEOPHYSICAL UNION |
| WOS记录号 | WOS:000777950700001 |
| 资助机构 | Science and Technology Development Fund, Macau SAR ; UM Research Grant ; Guangdong-Hong Kong-Macau Joint Laboratory Program ; CORE |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/173929]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Gao, Liang |
| 作者单位 | 1.Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China 2.Univ Macau, Dept Civil & Environm Engn, Macau, Peoples R China 3.Beijing Jiaotong Univ, Sch Civil Engn, Beijing, Peoples R China 4.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci Nat Resources Res, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yuanyuan,Li, Xu,Tang, Qiuhong,et al. An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China[J]. EARTH AND SPACE SCIENCE,2022,9(4):19. |
| APA | Zhou, Yuanyuan,Li, Xu,Tang, Qiuhong,Kuok, Sin Chi,Fei, Kai,&Gao, Liang.(2022).An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China.EARTH AND SPACE SCIENCE,9(4),19. |
| MLA | Zhou, Yuanyuan,et al."An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China".EARTH AND SPACE SCIENCE 9.4(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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