  In recent years, climate extremes such as floods have created significant
environmental and economic hazards for Australia, causing damage to the
environment and economy and losses of human and animal lives. An efficient
method of forecasting floods is crucial to limit this damage. Techniques for
flood prediction are currently based on hydrological, and hydrodynamic
(physically-based) numerical models. Machine learning methods that include deep
learning offer certain advantages over conventional physically based
approaches, including flexibility and accuracy. Deep learning methods have been
promising for predicting small to medium-sized climate extreme events over a
short time horizon; however, large flooding events present a critical
challenge. We present an ensemble-based machine learning approach that
addresses large-scale extreme flooding challenges using a switching mechanism
motivated by extreme-value theory for long-short-term-memory (LSTM) deep
learning models. We use a multivariate and multi-step time-series prediction
approach to predict streamflow for multiple days ahead in the major catchments
of Australia. The ensemble framework also employs static information to enrich
the time-series information, allowing for regional modelling across catchments.
Our results demonstrate enhanced prediction of streamflow extremes, with
notable efficacy for large flooding scenarios in the selected Australian
catchments. Through comparative analysis, our methodology underscores the
potential for deep learning models to revolutionise flood forecasting across
diverse regions.
