{"id":505,"date":"2019-02-21T11:06:56","date_gmt":"2019-02-21T10:06:56","guid":{"rendered":"http:\/\/wp.unil.ch\/geokdd\/?p=505"},"modified":"2020-03-20T18:17:55","modified_gmt":"2020-03-20T17:17:55","slug":"our-participation-at-egu-2019","status":"publish","type":"post","link":"https:\/\/wp.unil.ch\/geokdd\/2019\/02\/our-participation-at-egu-2019\/","title":{"rendered":"Our participation at EGU 2019"},"content":{"rendered":"<p style=\"text-align: justify\">We actively participate in the European Geoscience Meeting (EGU) 2019, in Vienna.<!--more--><\/p>\n<p style=\"text-align: justify\"><strong>Session convener:<\/strong><\/p>\n<ul style=\"text-align: justify\">\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/session\/30931\" target=\"_blank\" rel=\"noopener noreferrer\">Machine learning for geosciences: data exploration and modelling<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/session\/30959\" target=\"_blank\" rel=\"noopener noreferrer\">Big data and machine learning in geosciences<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/session\/31029\" target=\"_blank\" rel=\"noopener noreferrer\">Learning from spatial data: unveiling the geo-environment through quantitative approaches\u00a0<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/session\/31481\" target=\"_blank\" rel=\"noopener noreferrer\">Spatial and temporal patterns of wildfires: models, theory, and reality\u00a0<\/a><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><strong>Participation:\u00a0<\/strong><\/p>\n<ul>\n<li style=\"text-align: justify\"><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-9974.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Analysis of environmental time series complexity<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-2401.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Fisher-Shannon Complexity of High-Frequency Wind Speed in Urban<\/a><br \/>\n<a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-2401.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">area<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-2399.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Analysis of wind time series using network science and multifractal<\/a><br \/>\n<a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-2399.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">concept<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-12763.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">MFDFA R package: multifractal analysis for time series<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-2760.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Feature Selection Inspired by Geospatial Data Analysis<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-4886.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Feature selection using simple and efficient machine learning models.<\/a><br \/>\n<a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-4886.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Case studies and software tools<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-7977.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Application of Machine Learning for Wildfire Susceptibility Mapping in<\/a><br \/>\n<a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-7977.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Liguria (Italy)<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-8057.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Landslide susceptibility assessment using Machine Learning: the Valais<\/a><br \/>\n<a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-8057.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Canton (Switzerland) case study<\/a><\/li>\n<li><a href=\"https:\/\/meetingorganizer.copernicus.org\/EGU2019\/EGU2019-17260-2.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">The role of forest fires in land use\/land cover changes in Portugal<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>We actively participate in the European Geoscience Meeting (EGU) 2019, in Vienna.<\/p>\n","protected":false},"author":1001757,"featured_media":550,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[1],"tags":[9,4,7,8],"class_list":{"0":"post-505","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-uncategorized","8":"tag-abstract","9":"tag-conference","10":"tag-egu","11":"tag-sessions"},"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/posts\/505","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/users\/1001757"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/comments?post=505"}],"version-history":[{"count":0,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/posts\/505\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/media\/550"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/media?parent=505"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/categories?post=505"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.unil.ch\/geokdd\/wp-json\/wp\/v2\/tags?post=505"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}