[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 …
[HTML][HTML] Advancing hydrology through machine learning: insights, challenges, and future directions using the CAMELS, caravan, GRDC, CHIRPS, PERSIANN, NLDAS …
Machine learning (ML) applications in hydrology are revolutionizing our understanding and
prediction of hydrological processes, driven by advancements in artificial intelligence and …
prediction of hydrological processes, driven by advancements in artificial intelligence and …
Assessing the physical realism of deep learning hydrologic model projections under climate change
This study examines whether deep learning models can produce reliable future projections
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …
[HTML][HTML] The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …
models (abbreviated as δ or delta models) with regionalized deep-network-based …
Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method
Abstract Model development in groundwater simulation and physics informed deep learning
(DL) has been advancing separately with limited integration. This study develops a general …
(DL) has been advancing separately with limited integration. This study develops a general …
Explaining the mechanism of multiscale groundwater drought events: A new perspective from interpretable deep learning model
This study presents a new approach to understand the causes of groundwater drought
events with interpretable deep learning (DL) models. As prerequisites, accurate long short …
events with interpretable deep learning (DL) models. As prerequisites, accurate long short …
[HTML][HTML] Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures
Hydrological models are evaluated by comparisons with observed hydrological quantities
such as streamflow. A model evaluation procedure should account for dominantly epistemic …
such as streamflow. A model evaluation procedure should account for dominantly epistemic …
The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment
As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …
models (abbreviated as δ or delta models) with regionalized deep-network-based …
Streamflow prediction at the intersection of physics and machine learning: A case study of two Mediterranean‐climate watersheds
S Adera, D Bellugi, A Dhakal… - Water Resources …, 2024 - Wiley Online Library
Accurate streamflow predictions are essential for water resources management. Recent
studies have examined the use of hybrid models that integrate machine learning models …
studies have examined the use of hybrid models that integrate machine learning models …
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
E Acuña Espinoza, R Loritz… - Hydrology and Earth …, 2024 - hess.copernicus.org
Hydrological hybrid models have been proposed as an option to combine the enhanced
performance of deep learning methods with the interpretability of process-based models …
performance of deep learning methods with the interpretability of process-based models …