2 citations · 2 across the 2 of their papers we have counts for
3 papers
Accessing Convective Hazards Frequency Shift with Climate Change using Physics-Informed Machine Learning
Mikhail Mozikov, Ilya Makarov, Alexandr Bulkin +3
In this paper we discuss and address the challenges of predicting extreme atmospheric events like intense rainfall, hail, and strong winds. These events can cause significant damag…
Assessing the Risk of Permafrost Degradation with Physics-Informed Machine Learning
Polina Pilyugina, Timofey Chernikov, Alexey Zaytsev +6
Global warming accelerates permafrost degradation, impacting the reliability of critical infrastructure used by more than five million people daily. Furthermore, permafrost thaw pr…
Long-term drought prediction using deep neural networks based on geospatial weather data
Alexander Marusov, Vsevolod Grabar, Yury Maximov +3
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due t…