# Extreme Event Prediction

Flood warning for Australian catchments now comes as a 90% prediction band rather than a single flow curve: three independently trained LSTMs give the mean flow and the 5th and 95th percentiles, and the band's position against a 15% annual exceedance threshold becomes a high, moderate or low risk label. On CAMELS-AUS, the q=0.05 Quantile-LSTM reached the lowest RMSE of 0.0801 in South Australia against 0.0963 for a conventional LSTM, while peak timing error across states ran about 1.94 to 3.12 days with peaks underestimated by roughly 30%.

Flood warning for Australian catchments now comes as a 90% prediction band rather than a single flow curve: three independently trained LSTMs give the mean flow and the 5th and 95th percentiles, and the band's position against a 15% annual exceedance threshold becomes a high, moderate or low risk label. On CAMELS-AUS, the q=0.05 Quantile-LSTM reached the lowest RMSE of 0.0801 in South Australia against 0.0963 for a conventional LSTM, while peak timing error across states ran about 1.94 to 3.12 days with peaks underestimated by roughly 30%.

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.