BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data
文献类型：期刊论文
| 作者 | Xie, Chuanjie2; Huang, Chong2; Zhang, Deqiang1; He, Wei2 |
| 刊名 | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 18期号:19页码:12 |
| 关键词 | time series data imputation deep learning meteorological observation data |
| DOI | 10.3390/ijerph181910321 |
| 通讯作者 | Huang, Chong(huangch@lreis.ac.cn) |
| 英文摘要 | Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model's excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios. |
| WOS关键词 | MULTIVARIATE TIME-SERIES ; MISSING VALUE IMPUTATION ; INTERPOLATION ; MODEL |
| 资助项目 | CAS Earth Big Data Science Project[XDA19060302] ; Science and Technology Basic Resource Investigation Program of China[2017YFD0300403] |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:000727334900001 |
| 出版者 | MDPI |
| 资助机构 | CAS Earth Big Data Science Project ; Science and Technology Basic Resource Investigation Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/168508]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Chong |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Plant Resources Conservat & Sustainable U, South China Bot Garden, Guangzhou 510650, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Chuanjie,Huang, Chong,Zhang, Deqiang,et al. BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2021,18(19):12. |
| APA | Xie, Chuanjie,Huang, Chong,Zhang, Deqiang,&He, Wei.(2021).BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,18(19),12. |
| MLA | Xie, Chuanjie,et al."BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 18.19(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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