{"id":553198,"date":"2018-11-19T10:00:24","date_gmt":"2018-11-19T14:00:24","guid":{"rendered":"https:\/\/ntrs.nasa.gov\/search.jsp?R=20180007673"},"modified":"2018-11-19T10:00:24","modified_gmt":"2018-11-19T14:00:24","slug":"machine-learning-application-to-atmospheric-chemistry-modeling","status":"publish","type":"post","link":"https:\/\/spaceweekly.com\/?p=553198","title":{"rendered":"Machine Learning Application to Atmospheric Chemistry Modeling"},"content":{"rendered":"<p>Abstract: Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical cond&#8230;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS&#8230;<\/p>\n","protected":false},"author":60,"featured_media":615444,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-553198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/posts\/553198","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/users\/60"}],"replies":[{"embeddable":true,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=553198"}],"version-history":[{"count":1,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/posts\/553198\/revisions"}],"predecessor-version":[{"id":553199,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/posts\/553198\/revisions\/553199"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=\/wp\/v2\/media\/615444"}],"wp:attachment":[{"href":"https:\/\/spaceweekly.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=553198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=553198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/spaceweekly.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=553198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}