[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120304-en":3,"doc-seo-120304-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120304,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Forecasting Drought Phenomena Using a Statistical and Machine Learning-Based Analysis for the Central Anatolia Region, Turkey","Drought is a major concern in Turkey, strongly affecting agriculture, water resources, and the economy, particularly in Central Anatolia’s semiarid steppe and dry-sub-humid climate. The study develops an optimal forecasting approach for Standardised Precipitation Evapotranspiration Index (SPEI) values across multiple accumulation periods (1–24 months) using observations from 50 stations. Statistical and machine learning methods are compared, showing that machine learning—especially the Bayesian Recurrent Neural Network—outperforms traditional statistical models. Results indicate a steady rise in drought severity and robust model performance across SPEI periods. The work benchmarks future forecasting research and supports drought mitigation and adaptation planning.","University of Groningen  \nForecasting Drought Phenomena Using a Statistical and Machine Learning-Based Analysis for the Central Anatolia Region, Turkey  \nTürkeş, Murat; Özdemir, Ozancan; Yozgatlıgil, Ceylan  \nPublished in:  \nInternational Journal of Climatology  \nDOI:  \n10.1002/joc.8742  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nTürkeş, M. , Özdemir, O. , & Yozgatlıgil, C. (2025) . Forecasting Drought Phenomena Using a Statistical and Machine Learning-Based Analysis for the Central Anatolia Region, Turkey. International Journal of Climatology, 45(4), Article e8742 . [https://doi.org/10.1002/joc.8742](https://doi.org/10.1002/joc.8742)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nInternational Journal of Climatology  \nRESEARCH ARTICLE  \nForecasting Drought Phenomena Using a Statistical and Machine Learning-Based Analysis for the Central Anatolia Region, Turkey  \nMurat Türkeş1,2  | Ozancan Özdemir3,4 | Ceylan Yozgatlıgil3   \n1Bogazici University Center for Climate Change and Policy Studies, İstanbul, Türkiye | 2Bogazici University Institute of Science and Engineering, İstanbul, Türkiye | 3Department of Statistics, Middle East Technical University, Ankara, Türkiye | 4University of Groningen, Bernoulli Institute for Mathematics,  \nComputer Science and Artificial Intelligence, Groningen, Netherlands Correspondence: Murat Türkeş ([murat.turkes@boun.edu.tr](murat.turkes@boun.edu.tr))  \nReceived: 8 February 2024 | Revised: 5 November 2024 | Accepted: 17 December 2024  \nKeywords: climate variability and change | drought forecasting | machine learning | semi-arid steppe climate | Standardised Precipitation Evapotranspiration Index (SPEI) | statistical models | Turkey  \nABSTRACT  \nDrought is a major concern in Turkey, significantly affecting agriculture, water resources and the economy, especially in the Central Anatolia region with a semiarid steppe and dry-sub-humid climate. This study aims to develop an optimal forecasting model for Standardised Precipitation Evapotranspiration Index (SPEI) values over various periods (1–24 months) using data from 50 stations in the Central Anatolia region. It compares statistical forecasting and machine learning methods, finding that machine learning algorithms, particularly the Bayesian Recurrent Neural Network, outperform statistical approaches. The results show a consistent increase in drought severity and highlight the robust performance of top models across different SPEI periods. The s","cbCaiaDQLxhkGrJz","https://ap.wps.com/l/cbCaiaDQLxhkGrJz","pdf",18226818,1,26,"English","en",105,"# Introduction\n# Study Objectives and Data\n## Forecasting Setup Across SPEI Periods (1–24 months)\n# Methods: Statistical vs Machine Learning\n## Bayesian Recurrent Neural Network\n# Results and Model Performance\n## Drought Severity Across Periods","[{\"question\":\"What drought indicator does the study forecast in Central Anatolia?\",\"answer\":\"The study forecasts the Standardised Precipitation Evapotranspiration Index (SPEI) values for multiple accumulation periods.\"},{\"question\":\"How does the research compare forecasting approaches?\",\"answer\":\"It compares statistical forecasting methods with machine learning methods, evaluating their performance across different SPEI periods.\"},{\"question\":\"Which model performs best according to the results?\",\"answer\":\"Machine learning models perform better overall, with the Bayesian Recurrent Neural Network showing the strongest results.\"}]","Forecasting Drought Phenomena Using a Statistical and Machine Learning-Based Analysis for the Central Anatolia Region, Turkey | 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