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Deep learning methods for flood map**: a review of existing applications and future research directions
Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …
the limitations of accurate, yet slow, numerical models, and to improve the results of …
Explaining deep neural networks and beyond: A review of methods and applications
With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
What role does hydrological science play in the age of machine learning?
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 …
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
Toward improved predictions in ungauged basins: Exploiting the power of machine learning
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 …
ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS …
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales
Recent observations with varied schedules and types (moving average, snapshot, or
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …
Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments
Long short‐term memory (LSTM) networks represent one of the most prevalent deep
learning (DL) architectures in current hydrological modeling, but they remain black boxes …
learning (DL) architectures in current hydrological modeling, but they remain black boxes …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
Machine learning for hydrologic sciences: An introductory overview
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
[HTML][HTML] Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non …
It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate
and reliable groundwater level forecasts, which are an important tool for sustainable …
and reliable groundwater level forecasts, which are an important tool for sustainable …