{"id":661,"date":"2024-02-26T15:39:25","date_gmt":"2024-02-26T14:39:25","guid":{"rendered":"https:\/\/wp.unil.ch\/hydmet\/?page_id=661"},"modified":"2024-02-26T15:47:29","modified_gmt":"2024-02-26T14:47:29","slug":"generalization-of-an-encoder-decoder-lstm-model-for-flood-prediction-in-un-gauged-catchments","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/hydmet\/climate-rainfall-models\/generalization-of-an-encoder-decoder-lstm-model-for-flood-prediction-in-un-gauged-catchments\/","title":{"rendered":"An Encoder-Decoder LSTM model for un-gauged flood prediction"},"content":{"rendered":"\n<p>An Encoder-Decoder Long Short-Term Memory neural network is developed and tested for a 6-hour lead-time runoff prediction employed in un-gauged catchments. <\/p>\n\n\n<div class=\"wp-block-image is-resized\">\n<figure class=\"aligncenter size-large\"><img alt=\"\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"671\" src=\"https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg-1024x671.jpg\" alt=\"\" class=\"wp-image-662\" style=\"width:619px;height:auto\" title=\"\" srcset=\"https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg-1024x671.jpg 1024w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg-300x197.jpg 300w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg-768x503.jpg 768w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg-1536x1006.jpg 1536w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr2_lrg.jpg 1832w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">A schematic illustration of the ED-LSTM.<\/figcaption><\/figure>\n<\/div>\n\n<div class=\"wp-block-image is-resized\">\n<figure class=\"aligncenter size-large\"><img alt=\"\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"556\" src=\"https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-1024x556.jpg\" alt=\"\" class=\"wp-image-663\" style=\"width:659px;height:auto\" title=\"\" srcset=\"https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-1024x556.jpg 1024w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-300x163.jpg 300w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-768x417.jpg 768w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-1536x834.jpg 1536w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg-735x400.jpg 735w, https:\/\/wp.unil.ch\/hydmet\/files\/2024\/02\/1-s2.0-S0022169422011477-gr3_lrg.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Input composition and prediction process of the ED-LSTM structure for one event.<\/figcaption><\/figure>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\">References<\/h4>\n\n\n\n<p>Zhang, Y., Ragettli, S., Molnar, P., Fink, O., Peleg, N., <strong>Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments.<\/strong> Journal of Hydrology, 641(B), 128577.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Code<\/h4>\n\n\n\n<p><a href=\"https:\/\/github.com\/yikuizh\/edlstm_flood_prediction\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/yikuizh\/edlstm_flood_prediction<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>An Encoder-Decoder Long Short-Term Memory neural network is developed and tested for a 6-hour lead-time runoff prediction employed in un-gauged catchments. References Zhang, Y., Ragettli, S., Molnar, P., Fink, O., &hellip; <\/p>\n","protected":false},"author":1002277,"featured_media":0,"parent":199,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-661","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/pages\/661","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/users\/1002277"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/comments?post=661"}],"version-history":[{"count":4,"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/pages\/661\/revisions"}],"predecessor-version":[{"id":670,"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/pages\/661\/revisions\/670"}],"up":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/pages\/199"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/hydmet\/wp-json\/wp\/v2\/media?parent=661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}