Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Georgia Papacharalampous"'
Publikováno v:
Progress in Earth and Planetary Science, Vol 10, Iss 1, Pp 1-20 (2023)
Abstract Detailed investigations of time series features across climates, continents and variable types can progress our understanding and modelling ability of the Earth’s hydroclimate and its dynamics. They can also improve our comprehension of th
Externí odkaz:
https://doaj.org/article/6e7e3412c540487f91cc2912e9fec6c1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6969-6979 (2023)
Knowing the actual precipitation in space and time is critical in hydrological modeling applications, yet the spatial coverage with rain gauge stations is limited due to economic constraints. Gridded satellite precipitation datasets offer an alternat
Externí odkaz:
https://doaj.org/article/b11a1bcf6afd47a8b17bb7320af8316e
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035044 (2024)
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 optimally provide such estimates by benchmarking six algorithms, mostl
Externí odkaz:
https://doaj.org/article/23cdddd51805445bb5233f0473593de4
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4912 (2023)
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependen
Externí odkaz:
https://doaj.org/article/ebafb1eacd914249bdd3018eb01fbee7
Publikováno v:
Frontiers in Water, Vol 4 (2022)
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant toward addressing the major challenges of formalizing and optimi
Externí odkaz:
https://doaj.org/article/f8144b393e044e99965cb5c7727c4f45
Publikováno v:
Hydrology, Vol 10, Iss 2, p 50 (2023)
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and st
Externí odkaz:
https://doaj.org/article/3cad235542864f8cb5199a70708c0c92
Publikováno v:
Water, Vol 15, Iss 4, p 634 (2023)
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 they do not agree with ground-based measurements. An established means fo
Externí odkaz:
https://doaj.org/article/2ce2c47f402f4254b1c28a76f664853a
Publikováno v:
Journal of Marine Science and Engineering, Vol 10, Iss 9, p 1241 (2022)
Given the increasing intensity and frequency of flood events, and the casualties and cost associated with bridge collapse events, explaining the flood behavior for the collapse sites would be of great necessity. In this study, annual peak flows of tw
Externí odkaz:
https://doaj.org/article/a4f66fcac67f44c788ff38bd3fa663d5
Publikováno v:
Water, Vol 14, Iss 10, p 1657 (2022)
Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path
Externí odkaz:
https://doaj.org/article/a56ec063b4514257a17bc098c13bc50c
Publikováno v:
Geoscience Letters, Vol 5, Iss 1, Pp 1-19 (2018)
Abstract The simplest way to forecast geophysical processes, an engineering problem with a widely recognized challenging character, is the so-called “univariate time series forecasting” that can be implemented using stochastic or machine learning
Externí odkaz:
https://doaj.org/article/8eb1c19b2e9a4600bb9706b9bd22a1c3