[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128414-en":3,"doc-seo-128414-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128414,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye","This research presents a methodology for selecting suitable wind farm locations by integrating ensemble machine learning with Geographical Information Systems (GIS). Spatial factors influencing wind energy localization are processed and analyzed using natural, socio-economic, and environmental criteria. Four supervised models—Random Forest, K-Nearest Neighbor, Support Vector Machines, and Naive Bayes—classify geo-referenced points using wind speed, elevation, and slope. Model outputs are combined by intersection to reduce error and bias. The final GIS map identifies suitable and unsuitable sites across Türkiye to support sustainable energy deployment.","Research  \nWind farm sites selection using a machine learning approach and geographical information systems inTürkiye  \nOras Fadhil Khalaf1 · Osman Nuri Uçan2 · Naseem Adnan Alsamarai3  \nReceived: 8 November 2024 / Accepted: 6 March 2025  \n© The Author(s) 2025 OPEN  \nAbstract  \nThis research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future.  \nKeywords GIS · Machine learning · Wind farm · Machine learning · Sustainable energy  \n1 Introduction  \nAddressing the intertwined issues of global energy consumption and environmental impact is indeed critical for sustainable development. Implementing reforms to transition towards cleaner and more efficient energy sources is paramount. This involves not only reducing reliance on fossil fuels but also embracing renewable energy technologies such as solar, wind, and hydro power [1]. Wind energy is considered one of the most important sustainable energy sources due to  \n* Oras Fadhil Khalaf, [oras.fadil@uosamarra.edu.iq |](oras.fadil@uosamarra.edu.iq |1University of Samarra)[1](oras.fadil@uosamarra.edu.iq |1University of Samarra)[University of Samarra](oras.fadil@uosamarra.edu.iq |1University of Samarra), Samarra, Iraq. 2School of Engineering and Natural Sciences, Electrical and Electronics Engineering, Altınbaş University, Istanbul, Türkiye. 3Al Imam Al Aadum University College, Baghdad , Iraq.  \nDiscover Computing  \n(2025) 28:22  \n| [https://doi.org/10.1007/s10791-025-09511-7](https://doi.org/10.1007/s10791-025-09511-7)  \nthe availability of suitable wind resources, environmental conservation and low cost, which gives it a great opportunity for expansion, growth and development [2]. The world’s total installed renewable energy capacity and its share of the electricity grid have in","cbCainjQx6B1x4i7","https://ap.wps.com/l/cbCainjQx6B1x4i7","pdf",3401111,2,1,18,"English","en",105,"# Introduction\n## Renewable energy transition and wind energy importance\n## Role of site selection and spatial analysis with GIS\n## Integrating machine learning with GIS for decision support","[{\"question\":\"How does the study combine machine learning with GIS for wind farm site selection?\",\"answer\":\"The approach uses GIS to process spatial factors, then applies ensemble learning from four supervised models to classify geo-referenced locations. The final agreement is derived from the intersection of model results to improve consistency.\"},{\"question\":\"Which criteria and features are used for classifying suitable wind farm sites?\",\"answer\":\"The classification uses three site-specific criteria: wind speed, elevation, and slope. These are evaluated alongside additional natural, socio-economic, and environmental factors within the GIS workflow.\"},{\"question\":\"Why is an ensemble and intersection strategy used instead of relying on a single model?\",\"answer\":\"The intersection of model outputs aims to reduce classification error and avoid bias or variance present in individual models. This improves overall accuracy and decision reliability.\"}]","Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye | PDF",1785947377,45,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"wind-farm-sites-selection-using-a-machine-learning-approach-and-geographical-information-systems-in-turkiye","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/wind-farm-sites-selection-using-a-machine-learning-approach-and-geographical-information-systems-in-turkiye/128414/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the study combine machine learning with GIS for wind farm site selection?","Question",{"text":76,"@type":77},"The approach uses GIS to process spatial factors, then applies ensemble learning from four supervised models to classify geo-referenced locations. The final agreement is derived from the intersection of model results to improve consistency.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which criteria and features are used for classifying suitable wind farm sites?",{"text":81,"@type":77},"The classification uses three site-specific criteria: wind speed, elevation, and slope. These are evaluated alongside additional natural, socio-economic, and environmental factors within the GIS workflow.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is an ensemble and intersection strategy used instead of relying on a single model?",{"text":85,"@type":77},"The intersection of model outputs aims to reduce classification error and avoid bias or variance present in individual models. 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