Zobrazeno 1 - 10
of 180
pro vyhledávání: '"R. J. Abrahart"'
Autor:
Kisi, Ozgur1 (AUTHOR) kisi@erciyes.edu.tr, Cimen, Mesut2 (AUTHOR)
Publikováno v:
Hydrological Sciences Journal/Journal des Sciences Hydrologiques. 2010, Vol. 55 Issue 8, p1451-1452. 2p.
Autor:
Mesut Çimen, Ozgur Kisi
Publikováno v:
Hydrological Sciences Journal. 55:1451-1452
The authors would like to express their gratitude for the interest shown by the discussers (Abrahart et al., 2010) in the paper and for their comments on the subject. We have tried to clarify all t...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47d0058e698d7f1bb8486c5d21ba33f5
https://doi.org/10.1080/02626667.2010.529449
https://doi.org/10.1080/02626667.2010.529449
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
Autor:
Seidu, Jamel1 (AUTHOR) jseidu@umat.edu.gh, Ewusi, Anthony1 (AUTHOR), Kuma, Jerry Samuel Yaw1 (AUTHOR), Ziggah, Yao Yevenyo2 (AUTHOR), Voigt, Hans-Jurgen3 (AUTHOR)
Publikováno v:
International Journal of River Basin Management. Dec2023, Vol. 21 Issue 4, p639-650. 12p.