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Found 17 papers, 0 papers with code
Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning
This demonstrates the potential of stacking to improve probabilistic predictions in spatial interpolation and beyond.
Uncertainty estimation in satellite precipitation interpolation with machine learning
Compared to QR, LightGBM showed improved performance with respect to the quantile scoring rule by 11. 10%, followed by QRF (7. 96%), GRF (7. 44%), GBM (4. 64%) and QRNN (1. 73%).
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables.
Deep Huber quantile regression networks
The main idea is to train a deep learning algorithm with the Huber quantile regression function, which is consistent for the Huber quantile functional.
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products.
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale
Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature.
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period.
A review of predictive uncertainty estimation with machine learning
Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users.
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
Several machine learning concepts and methods are notably relevant towards addressing the major challenges of formalizing and optimizing probabilistic forecasting implementations, as well as the equally important challenge of identifying the most useful ones among these implementations.
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, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics.
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets
Precipitation and temperature features (e. g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features.
Hydroclimatic time series features at multiple time scales
A comprehensive understanding of the behaviours of the various geophysical processes and an effective evaluation of time series (else referred to as "stochastic") simulation models require, among others, detailed investigations across temporal scales.
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale
Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.
Probabilistic water demand forecasting using quantile regression algorithms
The results mostly favour the practical systems designed using the linear boosting algorithm, probably due to the presence of trends in the urban water flow time series.
Boosting algorithms in energy research: A systematic review
Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data.
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
In this work, we present and appraise a new simple and flexible methodology for hydrological time series forecasting.
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.
