{"id":81,"date":"2023-12-13T18:04:15","date_gmt":"2023-12-13T18:04:15","guid":{"rendered":"https:\/\/sites.tntech.edu\/lcasl\/?page_id=81"},"modified":"2023-12-13T18:08:20","modified_gmt":"2023-12-13T18:08:20","slug":"wildfire-prediction","status":"publish","type":"page","link":"https:\/\/sites.tntech.edu\/lcasl\/wildfire-prediction\/","title":{"rendered":"Wildfire Prediction"},"content":{"rendered":"\n<div style=\"height:var(--wp--preset--spacing--50)\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-group alignwide has-global-padding is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading alignwide has-text-align-center has-xx-large-font-size\" style=\"line-height:1.2\">WildFire Modelling with Satellite Image Stream<\/h3>\n<\/div>\n\n\n\n<div class=\"wp-block-group alignfull has-global-padding is-layout-constrained wp-container-core-group-is-layout-0747478d wp-block-group-is-layout-constrained\" style=\"padding-top:var(--wp--preset--spacing--50);padding-right:var(--wp--preset--spacing--50);padding-bottom:var(--wp--preset--spacing--50);padding-left:var(--wp--preset--spacing--50)\">\n<div class=\"wp-block-columns alignwide is-layout-flex wp-container-core-columns-is-layout-ff4b9c61 wp-block-columns-is-layout-flex\" style=\"margin-top:0;margin-bottom:0\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>As wildfires are expected to become more frequent and severe, improved prediction models are vital to mitigating risk and allocating resources. With remote sensing data, valuable spatiotemporal statistical models can be created and used for resource management practices. In this paper, we create a dynamic model for future wildfire predictions of five locations within the western United States through a deep neural network via historical burned area and climate data. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling. Between locations, local fire event triggers are not isolated, and there are confounding factors when local data is analyzed due to incomplete state observations. When compared to existing approaches that do not account for incomplete state observation within wildfire time-series data, on average, we are able to achieve higher prediction performances.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\">Dataset from the ten areas.<\/h2>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"741\" height=\"470\" src=\"https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/data_locations-1.png\" alt=\"\" class=\"wp-image-84\" style=\"width:943px;height:auto\" srcset=\"https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/data_locations-1.png 741w, https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/data_locations-1-300x190.png 300w\" sizes=\"auto, (max-width: 741px) 100vw, 741px\" \/><\/figure>\n\n\n\n<p>Selected rectangular grids map for data set generation from FIRMS. The rectangles are centered at Portland, Medford, Reno, Denver, Salt Lake City, etc.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\">Comupational Networks<\/h2>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"293\" src=\"https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/figure_computational_network-1024x293.jpg\" alt=\"\" class=\"wp-image-85\" style=\"width:1024px;height:auto\" srcset=\"https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/figure_computational_network-1024x293.jpg 1024w, https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/figure_computational_network-300x86.jpg 300w, https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/figure_computational_network-768x219.jpg 768w, https:\/\/sites.tntech.edu\/lcasl\/wp-content\/uploads\/sites\/163\/2023\/12\/figure_computational_network.jpg 1400w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Dynamic auto-encoder and fire map prediction network. The dynamic auto-encoder is trained to predict the observation at k+1 using the observation at k and the state of RNN. And the state of RNN is also used to predict fire map at T time steps ahead.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\">Online Prediction After Training<\/h2>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Multiple future predictions using data up to 2006-08-02.\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/Z_Jkr8yzVFA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Prediction of fire risk for selected seven train datasets (seven columns).&nbsp; And the&nbsp;<strong>top row<\/strong>&nbsp;shows the&nbsp;<strong>ground truth<\/strong>&nbsp;fire gridmap at the current time. The other rows show the predicted fire grid map. The&nbsp;<strong>2nd row<\/strong>&nbsp;is the&nbsp;<strong>prediction for k+1<\/strong>. The&nbsp;3rd row is the prediction for k+2 and so on.&nbsp; In this prediction, the step size is a week. The fire looks spreading from below to the top because the predictions are working.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Relevant Paper:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Hyung-Jin Yoon and Petros Voulgaris. &#8220;Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data,&#8221;\u00a0 IEEE International Conference on Knowledge Graph (2022 ICKG)<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>WildFire Modelling with Satellite Image Stream As wildfires are expected to become more frequent and severe, improved prediction models are vital to mitigating risk and allocating resources. With remote sensing data, valuable spatiotemporal statistical models can be created and used for resource management practices. In this paper, we create a dynamic model for future wildfire [&hellip;]<\/p>\n","protected":false},"author":184,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-81","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/pages\/81","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/users\/184"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/comments?post=81"}],"version-history":[{"count":3,"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/pages\/81\/revisions"}],"predecessor-version":[{"id":87,"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/pages\/81\/revisions\/87"}],"wp:attachment":[{"href":"https:\/\/sites.tntech.edu\/lcasl\/wp-json\/wp\/v2\/media?parent=81"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}