Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Heather McGrath"'
Autor:
Kelley Kilpatrick, Isabelle Savard, Li-Anne Audet, Gina Costanzo, Mariam Khan, Renée Atallah, Mira Jabbour, Wentao Zhou, Kathy Wheeler, Elissa Ladd, Deborah C Gray, Colette Henderson, Lori A Spies, Heather McGrath, Melanie Rogers
Publikováno v:
PLoS ONE, Vol 19, Iss 7, p e0305008 (2024)
IntroductionThe World Health Organization (WHO) called for the expansion of all nursing roles, including advanced practice nurses (APNs), nurse practitioners (NPs) and clinical nurse specialists (CNSs). A clearer understanding of the impact of these
Externí odkaz:
https://doaj.org/article/7aec82795b514d91948a587fc4cc3d92
A global perspective of advanced practice nursing research: A review of systematic reviews protocol.
Autor:
Kelley Kilpatrick, Isabelle Savard, Li-Anne Audet, Abby Kra-Friedman, Renée Atallah, Mira Jabbour, Wentao Zhou, Kathy Wheeler, Elissa Ladd, Deborah C Gray, Colette Henderson, Lori A Spies, Heather McGrath, Melanie Rogers
Publikováno v:
PLoS ONE, Vol 18, Iss 1, p e0280726 (2023)
IntroductionIn 2020, the World Health Organization called for the expansion and greater recognition of all nursing roles, including advanced practice nurses (APNs), to better meet patient care needs. As defined by the International Council of Nurses
Externí odkaz:
https://doaj.org/article/7a88c7aa73774f0ca7de7acb96bd79fa
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 113, Iss , Pp 102985- (2022)
Discrete Global Grid Systems (DGGS) have been increasingly adopted as a standard framework for multi-source geospatial data. Previous research largely studied the mathematical foundation of discrete global grids, developed open-source libraries, and
Externí odkaz:
https://doaj.org/article/38e6eea8cd1c4bc48a90ff81e0354e5b
Autor:
Heather McGrath, Piper Nora Gohl
Publikováno v:
Environmental Sciences Proceedings, Vol 25, Iss 1, p 18 (2023)
The emergence of Machine learning (ML) algorithms has shown competency in a variety of fields and are growing in popularity in their application to geospatial science issues. Most recently, and notably, ML algorithms have been applied to flood suscep
Externí odkaz:
https://doaj.org/article/e0c812e2c90a44c182f0de82a1271bfd
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 12, p 627 (2022)
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performan
Externí odkaz:
https://doaj.org/article/38d5784c7476496ebe9567b131b0b2af
Publikováno v:
Remote Sensing, Vol 14, Iss 23, p 6154 (2022)
Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect
Externí odkaz:
https://doaj.org/article/e1a12eb12f0b4258a2bb28d79fc26f40
Publikováno v:
Water, Vol 14, Iss 23, p 3801 (2022)
With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To
Externí odkaz:
https://doaj.org/article/9732e9cb1ac14478825879a33009b1c4
Autor:
Heather McGrath, Piper Nora Gohl
Publikováno v:
Remote Sensing, Vol 14, Iss 7, p 1656 (2022)
Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While sev
Externí odkaz:
https://doaj.org/article/faa11cba441c46019ca54c9f8c67709f
Publikováno v:
Remote Sensing, Vol 12, Iss 19, p 3206 (2020)
Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to pre
Externí odkaz:
https://doaj.org/article/dd53ac328d3c4da5bd4034936cbed31f
Autor:
Mathieu Turgeon-Pelchat, Heather McGrath, Fatemeh Esfahani, Simon Tolszczuk-Leclerc, Thomas Rainville, Nicolas Svacina, Lingjun Zhou, Zarrin Langari, Hospice Houngbo
The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4903c96405b717db4e6c83d6094119d
https://doi.org/10.5194/egusphere-egu23-9091
https://doi.org/10.5194/egusphere-egu23-9091
