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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 …
Global prediction of extreme floods in ungauged watersheds
Floods are one of the most common natural disasters, with a disproportionate impact in
develo** countries that often lack dense streamflow gauge networks. Accurate and timely …
develo** countries that often lack dense streamflow gauge networks. Accurate and timely …
Deep learning for water quality
Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …
context of intensifying climate extremes expected in the future. These challenges arise partly …
An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and …
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 …
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …
sustainability of water resources. The literature has shown great potential for nature-inspired …
[HTML][HTML] Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
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 …
[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
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 …