Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder
Climate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/5153 |
| _version_ | 1797606511869952000 |
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| author | Jiansong Tang Ruijia Yang Qiangsheng Dai Gaoteng Yuan Yingchi Mao |
| author_facet | Jiansong Tang Ruijia Yang Qiangsheng Dai Gaoteng Yuan Yingchi Mao |
| author_sort | Jiansong Tang |
| collection | DOAJ |
| description | Climate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in order to increase that capability is of great practical importance. The evaluation of meteorological disaster emergency response capability still has some issues. The majority of research methods use qualitative analysis, which makes it challenging to deal with fuzzy factors, leading to conclusions that are subjective and insufficiently rigorous. The evaluation models themselves are also complex and challenging to simulate and analyze, making it challenging to promote and use them in practice. Deep learning techniques have made it easier to collect and process large amounts of data, which has opened new avenues for advancement in the emergency management of weather-related disasters. In this paper, we suggest a Recurrent Neural Network (RNN)-based dynamic capability feature extraction method. The process of evaluation content determination and index selection is used to build a meteorological disaster emergency response capability evaluation index system before an encoder, based on the encoder–decoder architecture, is built for dynamic feature extraction. The RNN autoencoder deep learning ability dynamic rating method used in this paper has been shown through a series of experiments to be able to not only efficiently extract ability features from time series data and reduce the dimensionality of ability features, but also to reduce the focus of the ability evaluation model on simple and abnormal samples, concentrate the model learning on difficult samples, and have a higher accuracy. As a result, it is more suitable for the problem situation at evaluation of the disaster capability. |
| first_indexed | 2024-03-11T05:16:14Z |
| format | Article |
| id | doaj.art-b0679b6782f04f4e8e8a696662a28758 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:16:14Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b0679b6782f04f4e8e8a696662a287582023-11-17T18:14:22ZengMDPI AGApplied Sciences2076-34172023-04-01138515310.3390/app13085153Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN AutoencoderJiansong Tang0Ruijia Yang1Qiangsheng Dai2Gaoteng Yuan3Yingchi Mao4College of Computer and Information, Hohai University, Nanjing 211100, ChinaBusiness School, Hohai University, Nanjing 211100, ChinaState Grid Jiangsu Electric Power Company Ltd., Research Institute, Nanjing 211100, ChinaCollege of Computer and Information, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information, Hohai University, Nanjing 211100, ChinaClimate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in order to increase that capability is of great practical importance. The evaluation of meteorological disaster emergency response capability still has some issues. The majority of research methods use qualitative analysis, which makes it challenging to deal with fuzzy factors, leading to conclusions that are subjective and insufficiently rigorous. The evaluation models themselves are also complex and challenging to simulate and analyze, making it challenging to promote and use them in practice. Deep learning techniques have made it easier to collect and process large amounts of data, which has opened new avenues for advancement in the emergency management of weather-related disasters. In this paper, we suggest a Recurrent Neural Network (RNN)-based dynamic capability feature extraction method. The process of evaluation content determination and index selection is used to build a meteorological disaster emergency response capability evaluation index system before an encoder, based on the encoder–decoder architecture, is built for dynamic feature extraction. The RNN autoencoder deep learning ability dynamic rating method used in this paper has been shown through a series of experiments to be able to not only efficiently extract ability features from time series data and reduce the dimensionality of ability features, but also to reduce the focus of the ability evaluation model on simple and abnormal samples, concentrate the model learning on difficult samples, and have a higher accuracy. As a result, it is more suitable for the problem situation at evaluation of the disaster capability.https://www.mdpi.com/2076-3417/13/8/5153feature extractionencoder–decodermeteorological disasteremergency response capability |
| spellingShingle | Jiansong Tang Ruijia Yang Qiangsheng Dai Gaoteng Yuan Yingchi Mao Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder Applied Sciences feature extraction encoder–decoder meteorological disaster emergency response capability |
| title | Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder |
| title_full | Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder |
| title_fullStr | Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder |
| title_full_unstemmed | Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder |
| title_short | Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder |
| title_sort | research on feature extraction of meteorological disaster emergency response capability based on an rnn autoencoder |
| topic | feature extraction encoder–decoder meteorological disaster emergency response capability |
| url | https://www.mdpi.com/2076-3417/13/8/5153 |
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