Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM
文献类型：期刊论文
| 作者 | Yang, Xu; Zhuang, Ling; Sun, Yuqiang; Zhang, Wenjie |
| 刊名 | INTELLIGENT AUTOMATION AND SOFT COMPUTING
 |
| 出版日期 | 2023 |
| 卷号 | 37期号:2页码:1637-1654 |
| 关键词 | Thunderstorm point charge atmospheric electric field (AEF) recovery |
| ISSN号 | 1079-8587 |
| DOI | 10.32604/iasc.2023.039879 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | The loss of three-dimensional atmospheric electric field (3DAEF) data has a negative impact on thunderstorm detection. This paper proposes a method for thunderstorm point charge path recovery. Based on the relation-ship between a point charge and 3DAEF, we derive corresponding localization formulae by establishing a point charge localization model. Generally, point charge movement paths are obtained after fitting time series localization results. However, AEF data losses make it difficult to fit and visualize paths. Therefore, using available AEF data without loss as input, we design a hybrid model combining the convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) to predict and recover the lost AEF. As paths are not present during sunny weather, we propose an extreme gradient boosting (XGBoost) model combined with a stacked autoencoder (SAE) to further determine the weather conditions of the recovered AEF. Specifically, historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values (PVs). With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost, PV intervals corresponding to different weathers are acquired. The recovered AEF is then input into the fixed SAE-XGBoost model. Whether paths need to be fitted is determined by the interval to which the output PV belongs. The results confirm that the proposed method can effectively recover point charge paths, with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%. This method provides a valid reference for visual thunderstorm monitoring. |
| WOS关键词 | ATMOSPHERIC ELECTRIC-FIELD ; SURFACE |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001032466700024 |
| 出版者 | TECH SCIENCE PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194621]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Institute of Geographic Sciences & Natural Resources Research, CAS 2.Chinese Academy of Sciences 3.University of Alberta 4.Nanjing University of Information Science & Technology |
| 推荐引用方式 GB/T 7714 | Yang, Xu,Zhuang, Ling,Sun, Yuqiang,et al. Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM[J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING,2023,37(2):1637-1654. |
| APA | Yang, Xu,Zhuang, Ling,Sun, Yuqiang,&Zhang, Wenjie.(2023).Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM.INTELLIGENT AUTOMATION AND SOFT COMPUTING,37(2),1637-1654. |
| MLA | Yang, Xu,et al."Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM".INTELLIGENT AUTOMATION AND SOFT COMPUTING 37.2(2023):1637-1654. |
入库方式： OAI收割
来源：地理科学与资源研究所
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