Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
A comprehensive review of deep learning applications in hydrology and water resources
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …
variety and velocity of water-related data are increasing due to large-scale sensor networks …
An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and …
Z Yao, Z Wang, D Wang, J Wu, L Chen - Journal of Hydrology, 2023 - Elsevier
Accurate prediction of river runoff is of great significance for water resources management,
flood prevention and mitigation. The causes of runoff are complex and the mechanisms …
flood prevention and mitigation. The causes of runoff are complex and the mechanisms …
[HTML][HTML] Improving streamflow prediction in the WRF-Hydro model with LSTM networks
Researchers have attempted to use machine learning algorithms to replace physically
based models for streamflow prediction. Although existing studies have contributed to …
based models for streamflow prediction. Although existing studies have contributed to …
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 …
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 …
From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?
Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality
measure. Our capabilities of forecasting DO however remain elusive. Water quality data …
measure. Our capabilities of forecasting DO however remain elusive. Water quality data …
Machine learning and artificial intelligence to aid climate change research and preparedness
C Huntingford, ES Jeffers, MB Bonsall… - Environmental …, 2019 - iopscience.iop.org
Climate change challenges societal functioning, likely requiring considerable adaptation to
cope with future altered weather patterns. Machine learning (ML) algorithms have advanced …
cope with future altered weather patterns. Machine learning (ML) algorithms have advanced …
[HTML][HTML] Deep learning rainfall–runoff predictions of extreme events
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 …
a concern among hydrologists that the predictive accuracy of data-driven models based on …