{"id":59998,"date":"2025-05-29T00:00:05","date_gmt":"2025-05-28T21:00:05","guid":{"rendered":"https:\/\/geoconversation.org\/news\/a-neural-network-for-long-term-weather-and-flood-forecasts-has-been-developed-in-crimea\/"},"modified":"2025-05-29T00:00:05","modified_gmt":"2025-05-28T21:00:05","slug":"a-neural-network-for-long-term-weather-and-flood-forecasts-has-been-developed-in-crimea","status":"publish","type":"news","link":"https:\/\/geoconversation.org\/en\/news\/a-neural-network-for-long-term-weather-and-flood-forecasts-has-been-developed-in-crimea\/","title":{"rendered":"A neural network for long-term weather and flood forecasts has been developed in Crimea"},"content":{"rendered":"\n

Specialists<\/a> The Institute of Natural-Technical Systems in Sevastopol presented Russia\u2019s first neural network model capable of predicting precipitation, temperature and flood risks for up to nine months. The development is already helping to prevent droughts and floods in Crimea.<\/p>\n\n\n

Neural network<\/a> analyzes climate data over the past 70 years and updates forecasts monthly. It calculates average temperatures, precipitation and the probability of deviations from the norm. The model is now being tested in the area of \u200b\u200bMount Ai-Petri, a key source of fresh water for the peninsula.\u00a0<\/p>\n\n\n

\u201cThis is the only system in Russia that predicts not only the weather, but also flood conditions,\u201d explained developer Andrey Lubkov. \u201cFor example, it could warn in advance about a flood in Yalta in 2021 or a flood in Sevastopol in 2024.\u201d\u00a0<\/p>\n\n\n

The mountainous regions of Crimea receive the bulk of the precipitation that feeds the reservoirs. Long-term forecasts help avoid water shortages and prepare for extreme events.\u00a0<\/p>\n\n\n

Now neural network<\/a> expands its coverage area: it makes forecasts for Moscow, Tyumen, Ulan-Ude and even assesses the risks of the global climate phenomenon El Ni\u00f1o.\u00a0<\/p>\n\n\n

Scientists are working to create detailed maps for southern Russia and integrate the system into federal weather services. The research results will be presented at the international conference on geoecology of mountain rivers.\u00a0<\/p>\n\n\n

The new development opens up opportunities for early protection against natural disasters and rational management of water resources.\u00a0<\/p>\n\n\n

Source: geoinfo.ru<\/sub><\/p>\n\n\n

Image generated by a neural network<\/sub><\/p>\n\n\n

<\/p>\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Specialists The Institute of Natural-Technical Systems in Sevastopol presented Russia\u2019s first neural network model capable of predicting precipitation, temperature and flood risks for up to nine months. The development is already helping to prevent droughts and floods in Crimea. Neural network analyzes climate data ove<\/p>\n","protected":false},"author":9,"featured_media":17799,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"","_seopress_titles_title":"A neural network for long-term weather and flood forecasts has been developed in Crimea","_seopress_titles_desc":"Find out how a new neural network model predicts droughts and floods in Crimea. Read about its capabilities and benefits for the region.","_seopress_robots_index":"","_seopress_analysis_target_kw":"","footnotes":""},"categories":[126],"tags":[321],"class_list":["post-59998","news","type-news","status-publish","has-post-thumbnail","category-eco","tag-iskusstvennyj-intellekt-v-geologii"],"acf":[],"pbg_featured_image_src":{"full":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto.webp",1365,1024,false],"thumbnail":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-150x150.webp",150,150,true],"medium":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-300x225.webp",300,225,true],"medium_large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-768x576.webp",768,576,true],"large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-1024x768.webp",1024,768,true],"1536x1536":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto.webp",1365,1024,false],"2048x2048":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto.webp",1365,1024,false],"bricks_large_16x9":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-1200x675.webp",1200,675,true],"bricks_large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-1200x900.webp",1200,900,true],"bricks_large_square":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-1200x1024.webp",1200,1024,true],"bricks_medium":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-600x450.webp",600,450,true],"bricks_medium_square":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/05\/nejroset-pavodki-krym-foto-600x600.webp",600,600,true]},"pbg_author_info":{"display_name":"Lyubov Cherkasova","author_link":"https:\/\/geoconversation.org\/en\/author\/amourallis\/","author_img":false},"pbg_comment_info":" No Comments","pbg_excerpt":"Specialists The Institute of Natural-Technical Systems in Sevastopol presented Russia\u2019s first neural network model capable of predicting precipitation, temperature and flood risks for up to nine months. The development is already helping to prevent droughts and floods in Crimea. Neural network analyzes climate data ove","_links":{"self":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/news\/59998","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/types\/news"}],"author":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/comments?post=59998"}],"version-history":[{"count":0,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/news\/59998\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/media\/17799"}],"wp:attachment":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/media?parent=59998"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/categories?post=59998"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/tags?post=59998"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}