Downloads provided by UsageCounts
Hybrid streamflow modelling using machine learning and multi-model combination Global Hydrological model outputs that have been processed and divided into different validation setups in an effort to improve streamflow forecasts. The dataset included the following validation setups: all_stations, elbe, maas, elbe_catch,, maas_catch, rhine_catch, rhine_only, rhine_pcr. The GitHub repository contains comprehensive pre-processing instructions.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 16 | |
| downloads | 3 |

Views provided by UsageCounts
Downloads provided by UsageCounts