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Using artificial intelligence to manage extreme weather events
Can combining deep learning (DL) a subfield of artificial intelligence with social network analysis (SNA), make social media contributions about extreme weather events a useful tool for crisis managers, first responders and government scientists? An interdisciplinary team of McGill researchers has brought these tools to the forefront in an effort to understand and manage extreme weather events.
The researchers found that by using a noise reduction mechanism, valuable information could be filtered from social media to better assess trouble spots and assess users’ reactions vis-à-vis extreme weather events. The results of the study are published in the
Published 19 January 2021
Can combining deep learning (DL) a subfield of artificial intelligence with social network analysis (SNA), make social media contributions about extreme weather events a useful tool for crisis managers, first responders and government scientists?
Can combining deep learning (DL) a subfield of artificial intelligence with social network analysis (SNA), make social media contributions about extreme weather events a useful tool for crisis managers, first responders and government scientists? An interdisciplinary team of McGill researchers has brought these tools to the forefront in an effort to understand and manage extreme weather events.
The researchers found that by using a noise reduction mechanism, valuable information could be filtered from social media to better assess trouble spots and assess users’ reactions vis-à-vis extreme weather events. The results of the study are published in the