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[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 …
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical 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 …
Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins
The distribution of flowmeter data and basin characteristic information exhibits substantial
disparities, with most flow observations being recorded at a limited number of well …
disparities, with most flow observations being recorded at a limited number of well …
[HTML][HTML] Runoff modeling in ungauged catchments using machine learning algorithm-based model parameters regionalization methodology
H Wu, J Zhang, Z Bao, G Wang, W Wang, Y Yang… - Engineering, 2023 - Elsevier
Abstract Model parameters estimation is a pivotal issue for runoff modeling in ungauged
catchments. The nonlinear relationship between model parameters and catchment …
catchments. The nonlinear relationship between model parameters and catchment …
[HTML][HTML] Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds
Study region The study region is 23 different watersheds across Nepal. Study focus This
study aims at assessing the strengths and weaknesses of widely used regionalization …
study aims at assessing the strengths and weaknesses of widely used regionalization …
[HTML][HTML] Comparison of three daily rainfall-runoff hydrological models using four evapotranspiration models in four small forested watersheds with different land cover …
We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres
Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating …
Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating …
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 …
[HTML][HTML] Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches
Understanding hydrological processes is achieved using modeling approaches due to the
extensive and complex interactions between various environmental elements. Hydrological …
extensive and complex interactions between various environmental elements. Hydrological …
Hydrological simulation of ungauged basins via forcing by large‐scale hydrology models
C Skoulikaris, M Piliouras - Hydrological Processes, 2023 - Wiley Online Library
The established watershed's hydrologic simulation process, involving the calibration and
validation of a model through spatiotemporal streamflow observations, has recently been …
validation of a model through spatiotemporal streamflow observations, has recently been …
[HTML][HTML] Simulated changes in seasonal and low flows with climate change for Irish catchments
We assess changes in the seasonal mean and annual low flows (Q95) for 37 catchments
across the Republic of Ireland. Two hydrological models (SMART and GR4J) are trained …
across the Republic of Ireland. Two hydrological models (SMART and GR4J) are trained …