An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/601 |
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| author | Qi He Zihang Zhu Danfeng Zhao Wei Song Dongmei Huang |
| author_facet | Qi He Zihang Zhu Danfeng Zhao Wei Song Dongmei Huang |
| author_sort | Qi He |
| collection | DOAJ |
| description | Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery. |
| first_indexed | 2024-03-08T09:58:47Z |
| format | Article |
| id | doaj.art-6d354027f79544c8ab1ea7386b087f7f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:47Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6d354027f79544c8ab1ea7386b087f7f2024-01-29T13:43:01ZengMDPI AGApplied Sciences2076-34172024-01-0114260110.3390/app14020601An Interpretable Deep Learning Approach for Detecting Marine Heatwaves PatternsQi He0Zihang Zhu1Danfeng Zhao2Wei Song3Dongmei Huang4College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaMarine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery.https://www.mdpi.com/2076-3417/14/2/601sea surface temperaturemarine heat wavesexplainable artificial intelligence |
| spellingShingle | Qi He Zihang Zhu Danfeng Zhao Wei Song Dongmei Huang An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns Applied Sciences sea surface temperature marine heat waves explainable artificial intelligence |
| title | An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns |
| title_full | An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns |
| title_fullStr | An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns |
| title_full_unstemmed | An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns |
| title_short | An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns |
| title_sort | interpretable deep learning approach for detecting marine heatwaves patterns |
| topic | sea surface temperature marine heat waves explainable artificial intelligence |
| url | https://www.mdpi.com/2076-3417/14/2/601 |
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