A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting

17 June 2022

Papers citing "A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting"

8 / 8 papers shown

Title
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting Yanhong Li Jack L. Xu D. Anastasiu AI4TS 21 10 0 14 Dec 2023
Ensemble learning for blending gridded satellite and gauge-measured precipitation data Georgia Papacharalampous Hristos Tyralis N. Doulamis Anastasios Doulamis 17 8 0 09 Jul 2023
Deep Huber quantile regression networks Hristos Tyralis Georgia Papacharalampous N. Dogulu Kwok-Pan Chun UQCV 26 1 0 17 Jun 2023
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles Hristos Tyralis Georgia Papacharalampous N. Doulamis Anastasios Doulamis 13 5 0 02 Feb 2023
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale Georgia Papacharalampous Hristos Tyralis Anastasios Doulamis N. Doulamis 43 11 0 31 Dec 2022
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data Georgia Papacharalampous Hristos Tyralis Anastasios Doulamis N. Doulamis 8 15 0 17 Dec 2022
A review of predictive uncertainty estimation with machine learning Hristos Tyralis Georgia Papacharalampous UD UQCV 43 42 0 17 Sep 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015