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07/09/2023
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Regression algorithms are regularly used for improving the accuracy of s...
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06/17/2023
Deep Huber quantile regression networks
Typical machine learning regression applications aim to report the mean ...
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12/31/2022
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale
Merging satellite products and ground-based measurements is often requir...
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12/17/2022
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
Gridded satellite precipitation datasets are useful in hydrological appl...
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09/17/2022
A review of probabilistic forecasting and prediction with machine learning
Predictions and forecasts of machine learning models should take the for...
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06/17/2022
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
Probabilistic forecasting is receiving growing attention nowadays in a v...
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04/13/2022
Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents
Detailed feature investigations and comparisons across climates, contine...
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04/13/2022
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets
Regression-based frameworks for streamflow regionalization are built aro...
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02/26/2022
Hydrological post-processing for predicting extreme quantiles
Hydrological post-processing using quantile regression algorithms consti...
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01/14/2022
Expectile-based hydrological modelling for uncertainty estimation: Life after mean
Predictions of hydrological models should be probabilistic in nature. Ou...
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12/02/2021
Hydroclimatic time series features at multiple time scales
A comprehensive understanding of the behaviours of the various geophysic...
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10/11/2021
Quantile-based hydrological modelling
Predictive uncertainty in hydrological modelling is quantified by using ...
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07/25/2021
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale
Statistical analyses and descriptive characterizations are sometimes ass...
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04/16/2021
Probabilistic water demand forecasting using quantile regression algorithms
Machine and statistical learning algorithms can be reliably automated an...
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10/24/2020
Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity
Hydroclimatic time series analysis focuses on a few feature types (e.g.,...
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01/02/2020
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
Delivering useful hydrological forecasts is critical for urban and agric...
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09/09/2019
Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Daily streamflow forecasting through data-driven approaches is tradition...
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08/31/2019
Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale
Predictive hydrological uncertainty can be quantified by using ensemble ...
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08/31/2019