High Performance Computing and Artificial Intelligence for Geosciences
In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Sub...
I tiakina i:
| Hōputu: | Online |
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| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20230808_9783036581804_14 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869520725006614528 |
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| collection | Directory of Open Access Books |
| description | In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Subsequently, Huang et al. proposed a named entity recognition method for geological news based on BERT model. Yang et al. proposed an automatic landslide identification method to solve the problem of the time-consuming nature and low efficiency of traditional landslide identification methods. Du et al. analyzed the potential of unsupervised machine learning methods for submarine landslide prediction. Wang et al. performed parallel computations on the inversion algorithm of the two-dimensional ZTEM. Xu et al. used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides. Furthermore, Cao et al. proposed a new method called dual encoder transform (DualET) for the short-term prediction of photovoltaic power. Hao et al. conducted a series of parallel optimizations and large-scale parallel simulations on the high-resolution ocean model. Wang et al. proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion. Finally, Yang et al. developed a measure of site-level gross primary productivity (GPP) using the GeoMAN model. |
| format | Online |
| id | doab-20.500.12854ir-112508 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1125082024-03-30T12:51:07Z High Performance Computing and Artificial Intelligence for Geosciences Wang, Yuzhu Jiang, Jinrong Wang, Yangang Saint-Venant equations finite difference method parallel computing heterogeneous computing deep learning image enhancement mineral identification convolutional neural networks BERT named entity recognition geological news CRF semantic segmentation PSPNet landslide submarine landslide machine learning hazard susceptibility spatial distribution ZTEM 2D forward modeling inversion parallel algorithm tipper disaster precursor identification early warning association rule mining particle swarm optimization k-means clustering Apriori algorithm gray relation analysis transformer photovoltaic power forecasting satellite images LICOM meteorological model parallel optimization time series missing data tensor completion autoregressive norm displacement prediction GeoMAN model gross primary productivity attention mechanism interdisciplinary n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Subsequently, Huang et al. proposed a named entity recognition method for geological news based on BERT model. Yang et al. proposed an automatic landslide identification method to solve the problem of the time-consuming nature and low efficiency of traditional landslide identification methods. Du et al. analyzed the potential of unsupervised machine learning methods for submarine landslide prediction. Wang et al. performed parallel computations on the inversion algorithm of the two-dimensional ZTEM. Xu et al. used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides. Furthermore, Cao et al. proposed a new method called dual encoder transform (DualET) for the short-term prediction of photovoltaic power. Hao et al. conducted a series of parallel optimizations and large-scale parallel simulations on the high-resolution ocean model. Wang et al. proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion. Finally, Yang et al. developed a measure of site-level gross primary productivity (GPP) using the GeoMAN model. 2023-08-08T15:23:54Z 2023-08-08T15:23:54Z 2023 book ONIX_20230808_9783036581804_14 9783036581804 9783036581811 https://directory.doabooks.org/handle/20.500.12854/112508 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7627 https://mdpi.com/books/pdfview/book/7627 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8181-1 10.3390/books978-3-0365-8181-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036581804 9783036581811 188 Basel open access |
| spellingShingle | Saint-Venant equations finite difference method parallel computing heterogeneous computing deep learning image enhancement mineral identification convolutional neural networks BERT named entity recognition geological news CRF semantic segmentation PSPNet landslide submarine landslide machine learning hazard susceptibility spatial distribution ZTEM 2D forward modeling inversion parallel algorithm tipper disaster precursor identification early warning association rule mining particle swarm optimization k-means clustering Apriori algorithm gray relation analysis transformer photovoltaic power forecasting satellite images LICOM meteorological model parallel optimization time series missing data tensor completion autoregressive norm displacement prediction GeoMAN model gross primary productivity attention mechanism interdisciplinary n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries High Performance Computing and Artificial Intelligence for Geosciences |
| title | High Performance Computing and Artificial Intelligence for Geosciences |
| title_full | High Performance Computing and Artificial Intelligence for Geosciences |
| title_fullStr | High Performance Computing and Artificial Intelligence for Geosciences |
| title_full_unstemmed | High Performance Computing and Artificial Intelligence for Geosciences |
| title_short | High Performance Computing and Artificial Intelligence for Geosciences |
| title_sort | high performance computing and artificial intelligence for geosciences |
| topic | Saint-Venant equations finite difference method parallel computing heterogeneous computing deep learning image enhancement mineral identification convolutional neural networks BERT named entity recognition geological news CRF semantic segmentation PSPNet landslide submarine landslide machine learning hazard susceptibility spatial distribution ZTEM 2D forward modeling inversion parallel algorithm tipper disaster precursor identification early warning association rule mining particle swarm optimization k-means clustering Apriori algorithm gray relation analysis transformer photovoltaic power forecasting satellite images LICOM meteorological model parallel optimization time series missing data tensor completion autoregressive norm displacement prediction GeoMAN model gross primary productivity attention mechanism interdisciplinary n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Saint-Venant equations finite difference method parallel computing heterogeneous computing deep learning image enhancement mineral identification convolutional neural networks BERT named entity recognition geological news CRF semantic segmentation PSPNet landslide submarine landslide machine learning hazard susceptibility spatial distribution ZTEM 2D forward modeling inversion parallel algorithm tipper disaster precursor identification early warning association rule mining particle swarm optimization k-means clustering Apriori algorithm gray relation analysis transformer photovoltaic power forecasting satellite images LICOM meteorological model parallel optimization time series missing data tensor completion autoregressive norm displacement prediction GeoMAN model gross primary productivity attention mechanism interdisciplinary n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20230808_9783036581804_14 |