125 citations · 299 across the 21 of their papers we have counts for
23 papers
Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to…
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predic…
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis +1
Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that the…
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 +1
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are mo…
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Iason Katsamenis, Eftychios Protopapadakis, Nikolaos Bakalos +3
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying n…
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis +1
Knowing the actual precipitation in space and time is critical in hydrological modelling applications, yet the spatial coverage with rain gauge stations is limited due to economic…