Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data
Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2200 |
| _version_ | 1797503019131076608 |
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| author | Mingyue Lu Yadong Zhang Min Chen Manzhu Yu Menglong Wang |
| author_facet | Mingyue Lu Yadong Zhang Min Chen Manzhu Yu Menglong Wang |
| author_sort | Mingyue Lu |
| collection | DOAJ |
| description | Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions. |
| first_indexed | 2024-03-10T03:44:30Z |
| format | Article |
| id | doaj.art-fd83e0e3e7f448308ba3e204fcc836ef |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:30Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fd83e0e3e7f448308ba3e204fcc836ef2023-11-23T09:12:06ZengMDPI AGRemote Sensing2072-42922022-05-01149220010.3390/rs14092200Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial DataMingyue Lu0Yadong Zhang1Min Chen2Manzhu Yu3Menglong Wang4Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaDepartment of Geography, The Pennsylvania State University, University Park, PA 16802, USACollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaLightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.https://www.mdpi.com/2072-4292/14/9/2200monitoring lightning locationland attribute datadeep learningsensitivity analysis |
| spellingShingle | Mingyue Lu Yadong Zhang Min Chen Manzhu Yu Menglong Wang Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data Remote Sensing monitoring lightning location land attribute data deep learning sensitivity analysis |
| title | Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data |
| title_full | Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data |
| title_fullStr | Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data |
| title_full_unstemmed | Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data |
| title_short | Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data |
| title_sort | monitoring lightning location based on deep learning combined with multisource spatial data |
| topic | monitoring lightning location land attribute data deep learning sensitivity analysis |
| url | https://www.mdpi.com/2072-4292/14/9/2200 |
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