In this work, we develop a deep-learning generative model to offer a new visualization for the Alps and its glaciation over the last 120’000 years as if a satellite had passed over and taken high resolution images. This visualization relies on a new coupled climate-glacier evolution model, which uses the latest paleo-climate and ice thickness field reconstructions. The ultimate goal is for it to be used in the “IceAgeCam”, which is an interactive machine developed by the ZHDK, UZH and UNIL that aims to better inform the public about the cause of climate change in a long-term climatic context.
Statistical Analysis and Forecasting Model of Groundwater Fluctuation due to Rainfall
Parvez I.;Bovolenta R.;Riccomagno E.;Federici, B
2023-01-01
Abstract
In this work, we develop a deep-learning generative model to offer a new visualization for the Alps and its glaciation over the last 120’000 years as if a satellite had passed over and taken high resolution images. This visualization relies on a new coupled climate-glacier evolution model, which uses the latest paleo-climate and ice thickness field reconstructions. The ultimate goal is for it to be used in the “IceAgeCam”, which is an interactive machine developed by the ZHDK, UZH and UNIL that aims to better inform the public about the cause of climate change in a long-term climatic context.
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SGM_2023_Symposium_21.pdf
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Descrizione: Conference abstract entitled “Statistical Analysis and Forecasting Model of Groundwater Fluctuation due to Rainfall,” authored by Parvez, I., Boni, G., Bovolenta, R., Riccomagno, E., and Federici, B., and presented at the 21st Swiss Geoscience Meeting, Mendrisio, Switzerland, 2023. The study focuses on statistical analysis and forecasting of groundwater-level fluctuations in response to rainfall.
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