Open Access
| Issue |
SHS Web of Conf.
Volume 174, 2023
2023 2nd International Conference on Science Education and Art Appreciation (SEAA 2023)
|
|
|---|---|---|
| Article Number | 03008 | |
| Number of page(s) | 5 | |
| Section | Landscape Management and Socio-Environmental Planning | |
| DOI | https://doi.org/10.1051/shsconf/202317403008 | |
| Published online | 11 August 2023 | |
- Chen, X., Zhang, X., Church, J. et al. (2017) The increasing rate of global mean sea-level rise during 1993–2014. Nature Clim Change 7, 492–495. [CrossRef] [Google Scholar]
- Meier, M. F., Dyurgerov, M. B., Rick, U. K., O’Neel, S., Pfeffer, W. T., Anderson, R. S., Anderson, S. P., & Glazovsky, A. F. (2007). Glaciers Dominate Eu-static Sea-Level Rise in the 21st Century. Science, 317(5841), 1064–1067. [CrossRef] [Google Scholar]
- Wigley, Tom ML, and S. C. B. Raper. “Thermal expansion of sea water associated with global warming.” Nature 330.6144 (1987): 127-131. [CrossRef] [Google Scholar]
- Cutler, K. B., Edwards, R.L., Taylor, F.W. et al. (2003) Rapid sea-level fall and deep-ocean temperature change since the last interglacial period. Earth and Planetary Science Letters 206.3-4: 253-271. [CrossRef] [Google Scholar]
- Nieves, V., Radin, C. & Camps-Valls, G. (2021) Predicting regional coastal sea level changes with machine learning. Sci Rep 11, 7650. [CrossRef] [Google Scholar]
- Guillou, N., Chapalain, G., (2021) Machine learning methods applied to sea level predictions in the upper part of a tidal estuary, Oceanologia, Volume 63, Issue 4, 531-544. [CrossRef] [Google Scholar]
- Segal, M.R. (2004). Machine Learning Benchmarks and Random Forest Regression. Technical Report, Center for Bioinformatics & Molecular Biostatistics, University of California, San Francisco. [Google Scholar]
- Dietterich, T. G. (2002). Ensemble learning. The handbook of brain theory and neural networks, 2(1), 110-125. [Google Scholar]
- Xu, P., Jelinek, F. (2007) Random forests and the data sparseness problem in language modeling, Computer Speech & Language, Volume 21, Issue 1, 105-152. [CrossRef] [Google Scholar]
- Moradi, B., Aghapour, M., Shirbandi, A. (2022) Compare of Machine Learning and Deep Learning Approaches for Human Activity Recognition. In International Conference on Electrical Engineering, Tehran, Iran, Islamic Republic of, pp. 592-596 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.