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pro vyhledávání: '"William W. Hsieh"'
Autor:
William W. Hsieh
Publikováno v:
Environmental Data Science, Vol 1 (2022)
The growth of machine learning (ML) in environmental science can be divided into a slow phase lasting till the mid-2010s and a fast phase thereafter. The rapid transition was brought about by the emergence of powerful new ML methods, allowing ML to s
Externí odkaz:
https://doaj.org/article/cbde8f89e27646c3941da44b1cd87457
Autor:
William W. Hsieh
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applic
Autor:
William W. Hsieh
Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed3a019273de20ae7fcaff4ecb6fbb75
https://doi.org/10.1017/9781107588493
https://doi.org/10.1017/9781107588493
Autor:
Sue Ellen Haupt, David John Gagne, William W. Hsieh, Vladimir Krasnopolsky, Amy McGovern, Caren Marzban, William Moninger, Valliappa Lakshmanan, Philippe Tissot, John K. Williams
Publikováno v:
Bulletin of the American Meteorological Society. 103:E1351-E1370
Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b20fea0e60014d75485bdf24d31c1b02
https://doi.org/10.1175/bams-d-20-0234.1
https://doi.org/10.1175/bams-d-20-0234.1
Publikováno v:
The Cryosphere, Vol 12, Pp 891-905 (2018)
Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate muc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fe93ccda4d1442f5f87c014822a71aa
https://www.the-cryosphere.net/12/891/2018/tc-12-891-2018.pdf
https://www.the-cryosphere.net/12/891/2018/tc-12-891-2018.pdf
Publikováno v:
Journal of Hydrology. 555:983-994
In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::540c7d88247b04017ce0842d22dc31dd
https://doi.org/10.1016/j.jhydrol.2017.10.037
https://doi.org/10.1016/j.jhydrol.2017.10.037
Publikováno v:
Journal of Hydrology. 541:714-726
Regional-scale estimates of snow water equivalent (SWE) are challenging in alpine regions, particularly in areas of high accumulation and dense forest cover, suggesting efforts to improve these estimates may benefit from an evaluation of existing gri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4952379ee28bdcd63f0b90e0a9bea369
https://doi.org/10.1016/j.jhydrol.2016.07.027
https://doi.org/10.1016/j.jhydrol.2016.07.027
Publikováno v:
Air Quality, Atmosphere & Health. 10:195-211
Air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O3), particulate matter 2.5 μm (PM2.5), and nitrogen dioxide (NO2), up to 48 h for six stations across Canada—Vancouver, Edmonton, Wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fb924587e357370b712fc320580fcd2
https://doi.org/10.1007/s11869-016-0414-3
https://doi.org/10.1007/s11869-016-0414-3
Publikováno v:
Journal of Hydrology. 537:431-443
Summary While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0532bcff1fd160bf0c57141bc38f6814
https://doi.org/10.1016/j.jhydrol.2016.03.017
https://doi.org/10.1016/j.jhydrol.2016.03.017
Publikováno v:
Agricultural and Forest Meteorology. :74-84
Crop yield forecast models for barley, canola and spring wheat grown on the Canadian Prairies were developed using vegetation indices derived from satellite data and machine learning methods. Hierarchical clustering was used to group the crop yield d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::809fe2baa5ea283844d67c9ac9aff0eb
https://doi.org/10.1016/j.agrformet.2015.11.003
https://doi.org/10.1016/j.agrformet.2015.11.003