Zobrazeno 1 - 7
of 7
pro vyhledávání: '"R. J. Abrahart"'
Publikováno v:
Hydrology and Earth System Sciences, Vol 17, Iss 7, Pp 2827-2843 (2013)
In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-
Externí odkaz:
https://doaj.org/article/a85b0d5bcf74453faed47e041dcb85b8
Publikováno v:
Hydrology and Earth System Sciences, Vol 16, Iss 8, Pp 3049-3060 (2012)
When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistenc
Externí odkaz:
https://doaj.org/article/0697e276978a4fc38357206635f062b3
Autor:
R. J. Abrahart, L. M. See
Publikováno v:
Hydrology and Earth System Sciences, Vol 11, Iss 5, Pp 1563-1579 (2007)
Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential
Externí odkaz:
https://doaj.org/article/b3fc7ab27e1445d5ae98a5cb7e9e880b
Autor:
R. J. Abrahart, L. See
Publikováno v:
Hydrology and Earth System Sciences, Vol 6, Iss 4, Pp 655-670 (2002)
This paper evaluates six published data fusion strategies for hydrological forecasting based on two contrasting catchments: the River Ouse and the Upper River Wye. The input level and discharge estimates for each river comprised a mixed set of single
Externí odkaz:
https://doaj.org/article/6be4940a2a4049c5a5fc0227e4f96d5b
Publikováno v:
Geomorphology. 24:35-49
Two novel soil erosion models have been developed within the MEDALUS projects. Their principal innovation lies in the explicit treatment of long-term interactions with the vegetation and soil, with implications for the way in which the surface flow a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a91d6c92d4b58a854c6a0075db076a0
https://doi.org/10.1016/s0169-555x(97)00099-8
https://doi.org/10.1016/s0169-555x(97)00099-8
Publikováno v:
Environmental Health and Biomedicine.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5288ea3683d70e93f9ebf875745471df
https://doi.org/10.2495/ehr110081
https://doi.org/10.2495/ehr110081
Autor:
R. J. Abrahart, L. M. See
The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57fd686f9e893a3d8ce5c198caa2d72b
https://hal.archives-ouvertes.fr/hal-00298815
https://hal.archives-ouvertes.fr/hal-00298815
