Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Thomas Lees"'
Publikováno v:
Remote Sensing, Vol 14, Iss 3, p 698 (2022)
East Africa has experienced a number of devastating droughts in recent decades, including the 2010/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respond to emergi
Externí odkaz:
https://doaj.org/article/7d26c993d8784c50b45aa2c87e66a964
Autor:
Chris Huntingford, Elizabeth S Jeffers, Michael B Bonsall, Hannah M Christensen, Thomas Lees, Hui Yang
Publikováno v:
Environmental Research Letters, Vol 14, Iss 12, p 124007 (2019)
Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, an
Externí odkaz:
https://doaj.org/article/3c4ff648f9bf4d1a9425e5df0ff4c884
Autor:
Callum Munday, Sebastian Engelstaedter, Gilbert Ouma, Geoffrey Ogutu, Daniel Olago, Dennis Ong’ech, Thomas Lees, Bonface Wanguba, Rose Nkatha, Clinton Ogalo, Roba Ali Gàlgalo, Abdi Jillo Dokata, Erick Kirui, Robert Hope, Richard Washington
Publikováno v:
Bulletin of the American Meteorological Society. 103:E1828-E1842
The Turkana low-level jet (LLJ) is an intrinsic part of the African climate system. It is the principle conduit for water vapor transport to the African interior from the Indian Ocean, and droughts in East Africa tend to occur when the jet is strong.
By 2050, over half the world's population will live in water-stressed areas. Medium-term drought forecasting can help planners avoid ``day-zero'' events and adapt to climate change. Machine learning-based precipitation-runoff modelling enables the pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45da5d4b91be2796f3945d5f6800676b
https://doi.org/10.31223/x5v06f
https://doi.org/10.31223/x5v06f
Autor:
Wardrop, Joshua Thomas Lees
This thesis represents the cumulation of an industrially based project (KTP 10134) to find resource within manufacturing waste and develop a circular economy within industry. This work documents this collaborative project between Lancaster University
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c1f1e71dd24f09668f1f41041ecf942
Autor:
Frederik Kratzert, Thomas Lees, Reetik Kumar Sahu, Simon Dadson, Martin Gauch, Louise J. Slater, Peter Greve, Daniel Klotz, Steven Reece, Jens de Bruijn
Publikováno v:
Hydrology and Earth System Sciences, 26(12), 3079-3101. European Geosciences Union
Lees, T, Reece, S, Kratzert, F, Klotz, D, Gauch, M, De Bruijn, J, Kumar Sahu, R, Greve, P, Slater, L & Dadson, S J 2022, ' Hydrological concept formation inside long short-term memory (LSTM) networks ', Hydrology and Earth System Sciences, vol. 26, no. 12, pp. 3079-3101 . https://doi.org/10.5194/hess-26-3079-2022
Lees, T, Reece, S, Kratzert, F, Klotz, D, Gauch, M, De Bruijn, J, Kumar Sahu, R, Greve, P, Slater, L & Dadson, S J 2022, ' Hydrological concept formation inside long short-term memory (LSTM) networks ', Hydrology and Earth System Sciences, vol. 26, no. 12, pp. 3079-3101 . https://doi.org/10.5194/hess-26-3079-2022
Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30c8c786debce4b9ef2da16c417899a1
https://hess.copernicus.org/preprints/hess-2021-566/
https://hess.copernicus.org/preprints/hess-2021-566/