no code implementations • 15 Dec 2020 • Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Günter Klambauer, Sepp Hochreiter, Grey Nearing
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.
1 code implementation • 15 Oct 2020 • Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, Sepp Hochreiter
Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy.
no code implementations • 5 Jun 2020 • Martin Gauch, Jimmy Lin
In recent years, the paradigms of data-driven science have become essential components of physical sciences, particularly in geophysical disciplines such as climatology.
1 code implementation • 17 Nov 2019 • Martin Gauch, Juliane Mai, Jimmy Lin
Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements.
