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 …
A review of hybrid deep learning applications for streamflow forecasting
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …
applications have garnered significant interest in the hydrological community. Despite the …
Hydrogen jet and diffusion modeling by physics-informed graph neural network
Abstract Renewable Power-to-Hydrogen (P2H2) system is an emerging decarbonization
strategy for achieving global carbon neutrality. However, the propensity of hydrogen to leak …
strategy for achieving global carbon neutrality. However, the propensity of hydrogen to leak …
Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning
Recently, rainfall‐runoff simulations in small headwater basins have been improved by
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
Rapid flood inundation forecast using Fourier neural operator
Flood inundation forecast provides critical information for emergency planning before and
during flood events. Real time flood inundation forecast tools are still lacking. High …
during flood events. Real time flood inundation forecast tools are still lacking. High …
Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder
MS Jahangir, J Quilty - Journal of Hydrology, 2024 - Elsevier
Probabilistic hydrological forecasting has gained increasing importance in recent years, as it
offers essential information for risk-based decision-making and flood management …
offers essential information for risk-based decision-making and flood management …
Enhancing hydrological data completeness: a performance evaluation of various machine learning techniques using probabilistic fusion imputer with neural networks …
The present-day accessibility of streamflow data, particularly in the developing countries, is
often marked by a multitude of data shortfalls or distortions. This study investigates the …
often marked by a multitude of data shortfalls or distortions. This study investigates the …
[HTML][HTML] Simulation of spring discharge using graph neural networks at Niangziguan Springs, China
Y Gai, M Wang, Y Wu, E Wang, X Deng, Y Liu… - Journal of …, 2023 - Elsevier
Over past decades, extensive groundwater development in karst aquifers has led to
significant declines in groundwater levels and spring discharges. While the recent …
significant declines in groundwater levels and spring discharges. While the recent …
Stream temperature prediction in a shifting environment: Explaining the influence of deep learning architecture
Stream temperature is a fundamental control on ecosystem health. Recent efforts
incorporating process guidance into deep learning models for predicting stream temperature …
incorporating process guidance into deep learning models for predicting stream temperature …
Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …
challenge for water resources science. The majority of the world's freshwater resources have …