This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …

Long short‐term memory (LSTM) networks offer unprecedented accuracy for prediction in
ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS …

The most accurate rainfall–runoff predictions are currently based on deep learning. There is
a concern among hydrologists that the predictive accuracy of data-driven models based on …

Floods are one of the most common natural disasters, with a disproportionate impact in
developing countries that often lack dense streamflow gauge networks. Accurate and timely …

With recent developments in computational techniques, Data-driven Machine Learning
Models (DMLs) have shown great potential in simulating streamflow and capturing the …

Despite showing great success of applications in many commercial fields, machine learning
and data science models generally show limited success in many scientific fields, including …

Unprecedented changes in the climate and environment have been observed in the three
poles, including the North Pole, the South Pole, and the Third Pole–Tibetan Plateau …

Spatial data mining helps to find hidden but potentially informative patterns from large and
high-dimensional geoscience data. Non-spatial learners generally look at the observations …

Due to a small proportion of observations, reliable and accurate flood forecasts for large
floods present a fundamental challenge to artificial neural network models, especially when …

The particularities of geosystems and geoscience data must be understood before any
development or implementation of statistical learning algorithms. Without such knowledge …