AI for Marine, Ocean and Climate Change Monitoring
The oceans play a pivotal role in regulating the Earth's climate, absorbing excess heat with far-reaching consequences such as rising sea levels and shifts in ocean circulation. To address these complex challenges, there is a growing interest in using advanced statistical, machine learning, and AI t...
Gorde:
| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20240514_9783036599984_78 |
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Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869529416303902720 |
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| collection | Directory of Open Access Books |
| description | The oceans play a pivotal role in regulating the Earth's climate, absorbing excess heat with far-reaching consequences such as rising sea levels and shifts in ocean circulation. To address these complex challenges, there is a growing interest in using advanced statistical, machine learning, and AI techniques to observe and model these ocean processes from space. This approach holds immense potential for identifying and predicting these intricate mechanisms, providing valuable insights into the impacts of climate change. This Special Issue reprint is dedicated to advancing climate science by integrating machine learning, remote sensing, and oceanography. It explores the application of cutting-edge technologies such as artificial neural networks and data-driven algorithms to skillfully analyze and forecast ocean-related processes. These cutting-edge techniques are essential for the challenges posed by ocean warming and its effects, emphasizing the urgent need for interdisciplinary research that combines expertise in AI, machine learning, and earth sciences. By fostering innovation and knowledge exchange, this Special Issue compiles recent advancements in ocean and climate sciences. It offers a wide array of methodological perspectives and tools to enhance our understanding of global and regional climate change monitoring, elevate forecasting capabilities, and clarify sources of uncertainty in predictive models. This effort signifies a crucial step in addressing the challenges arising from technological gaps and the impacts of climate change on our oceans and the planet. |
| format | Online |
| id | doab-20.500.12854ir-137476 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1374762024-05-14T13:12:43Z AI for Marine, Ocean and Climate Change Monitoring Nieves, Veronica Ruescas, Ana B. Sauzède, Raphaëlle earth observations ocean dynamics satellite altimetry sea surface temperature artificial intelligence machine learning deep learning neural networks salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning long short-term memory (LSTM) sea surface temperature (SST) East China Sea interpolation data-driven models variational data assimilation missing data suspended particulate matter observing system experiment Bay of Biscay near-surface humidity remote sensing China Seas sea temperature prediction reconstructed sea subsurface temperature data 3D U-Net LSTM chlorophyll-a cloud classification MODIS ocean color Sargassum MSI OLCI Sentinel-2 Sentinel-3 convolutional neural network spatiotemporal prediction graph neural network BGC-Argo ED380 ED412 ED490 global ocean light models neural network PAR n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics The oceans play a pivotal role in regulating the Earth's climate, absorbing excess heat with far-reaching consequences such as rising sea levels and shifts in ocean circulation. To address these complex challenges, there is a growing interest in using advanced statistical, machine learning, and AI techniques to observe and model these ocean processes from space. This approach holds immense potential for identifying and predicting these intricate mechanisms, providing valuable insights into the impacts of climate change. This Special Issue reprint is dedicated to advancing climate science by integrating machine learning, remote sensing, and oceanography. It explores the application of cutting-edge technologies such as artificial neural networks and data-driven algorithms to skillfully analyze and forecast ocean-related processes. These cutting-edge techniques are essential for the challenges posed by ocean warming and its effects, emphasizing the urgent need for interdisciplinary research that combines expertise in AI, machine learning, and earth sciences. By fostering innovation and knowledge exchange, this Special Issue compiles recent advancements in ocean and climate sciences. It offers a wide array of methodological perspectives and tools to enhance our understanding of global and regional climate change monitoring, elevate forecasting capabilities, and clarify sources of uncertainty in predictive models. This effort signifies a crucial step in addressing the challenges arising from technological gaps and the impacts of climate change on our oceans and the planet. 2024-05-14T13:12:34Z 2024-05-14T13:12:34Z 2024 book ONIX_20240514_9783036599984_78 9783036599984 9783036599977 https://directory.doabooks.org/handle/20.500.12854/137476 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8633 https://mdpi.com/books/pdfview/book/8633 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-9997-7 10.3390/books978-3-0365-9997-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036599984 9783036599977 230 open access |
| spellingShingle | earth observations ocean dynamics satellite altimetry sea surface temperature artificial intelligence machine learning deep learning neural networks salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning long short-term memory (LSTM) sea surface temperature (SST) East China Sea interpolation data-driven models variational data assimilation missing data suspended particulate matter observing system experiment Bay of Biscay near-surface humidity remote sensing China Seas sea temperature prediction reconstructed sea subsurface temperature data 3D U-Net LSTM chlorophyll-a cloud classification MODIS ocean color Sargassum MSI OLCI Sentinel-2 Sentinel-3 convolutional neural network spatiotemporal prediction graph neural network BGC-Argo ED380 ED412 ED490 global ocean light models neural network PAR n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics AI for Marine, Ocean and Climate Change Monitoring |
| title | AI for Marine, Ocean and Climate Change Monitoring |
| title_full | AI for Marine, Ocean and Climate Change Monitoring |
| title_fullStr | AI for Marine, Ocean and Climate Change Monitoring |
| title_full_unstemmed | AI for Marine, Ocean and Climate Change Monitoring |
| title_short | AI for Marine, Ocean and Climate Change Monitoring |
| title_sort | ai for marine ocean and climate change monitoring |
| topic | earth observations ocean dynamics satellite altimetry sea surface temperature artificial intelligence machine learning deep learning neural networks salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning long short-term memory (LSTM) sea surface temperature (SST) East China Sea interpolation data-driven models variational data assimilation missing data suspended particulate matter observing system experiment Bay of Biscay near-surface humidity remote sensing China Seas sea temperature prediction reconstructed sea subsurface temperature data 3D U-Net LSTM chlorophyll-a cloud classification MODIS ocean color Sargassum MSI OLCI Sentinel-2 Sentinel-3 convolutional neural network spatiotemporal prediction graph neural network BGC-Argo ED380 ED412 ED490 global ocean light models neural network PAR n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| topic_facet | earth observations ocean dynamics satellite altimetry sea surface temperature artificial intelligence machine learning deep learning neural networks salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning long short-term memory (LSTM) sea surface temperature (SST) East China Sea interpolation data-driven models variational data assimilation missing data suspended particulate matter observing system experiment Bay of Biscay near-surface humidity remote sensing China Seas sea temperature prediction reconstructed sea subsurface temperature data 3D U-Net LSTM chlorophyll-a cloud classification MODIS ocean color Sargassum MSI OLCI Sentinel-2 Sentinel-3 convolutional neural network spatiotemporal prediction graph neural network BGC-Argo ED380 ED412 ED490 global ocean light models neural network PAR n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| url | ONIX_20240514_9783036599984_78 |