[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121763-en":3,"doc-seo-121763-105":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},121763,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Initial observations from machine learning approaches using UK temperature data for mine water thermal","Machine learning offers a faster way to synthesize large volumes of subsurface temperature data, yet it has seen limited uptake in UK mine water thermal applications. This work applies k-means clustering and multiple linear regression to a UK dataset combining 2.4+ million temperature datapoints from 800 locations from multiple public sources plus licensed Coal Authority data. Geothermal gradients of 22–23°C/km are predicted with R²>0.85, and mine waters appear ~2°C warmer than unmined aquifers. Statistical significance is reported alongside uncertainty linked to pumping-borehole and shaft temperature mixing, highlighting the need for broader data sharing to better address resource uncertainties.","Initial observations from machine learning approaches using UK temperature data for mine  \nwater thermal (MWT)  \nSally F. Jack 1, Neil M. Burnside 1, Catherine M. Hirst2, Sara Mehrabi2, and Zoe K. Shipton 1  \n1Department of Civil & Environmental Engineering, University of Strathclyde, G1 1XJ, UK  \n2COWI UK Limited, Glasgow, G2 5RG, UK  \n[sally.jack.2020@uni.strath.ac.uk](sally.jack.2020@uni.strath.ac.uk)  \nKeywords: Mine water thermal, shallow geothermal, machine learning, artificial intelligence, temperature data  \nABSTRACT  \nUntil now, machine learning has been underutilised in shallow geothermal and more specifically, mine water thermal applications. Mine water thermal is a low-carbon, self-replenishing solution to providing space heating and cooling yet does not have significant uptake in the UK relative to other European country counterparts. This slower development is partly due to a lack of understanding of heat movement and behaviour in existing and abandoned mine systems. Where machine learning could offer a different perspective to more traditionally used numerical modelling techniques is in its ability for algorithms to quickly synthesise and process large volumes of available data. In this paper, we present machine learning methods applied to a UK subsurface temperature dataset to begin to minimise some of the knowledge gaps currently hindering development. We collated publicly available temperature data from The Coal Authority, British Geological Survey, Glasgow UK GeoEnergy Observatory, Scottish Environment Protection Agency, and the North Sea Transition Authority alongside additional licenced data from The Coal Authority, to form a dataset with over 2.4 million datapoints from 800 distinct locations. Both k-means clustering and multiple linear regression algorithms arepresented alongside preliminary results of three models where geothermal gradients are predicted. The average geothermal gradient predicted by these models was 22°C/km (model A) and 23°C/km (model B and C), which is lower than the average UK-wide geothermal gradient of ~26°C/km modelled by previous authors. Furthermore, mine water temperatures were predicted to be 2°C warmer than other groundwaters in unmined aquifers at the same depth. All models were statistically significant with R-squared values of >0.85 . We suggest that the data is likely presenting skewed results due to the inclusion of temperatures taken from pumping boreholes and mine shafts that mixes the warmer water at depth with colder, shallower water, creating a more suppressed temperature with depth. The simple relationships between temperature and depth illustrated in this paper form the beginnings ofa machine learning platform from which more features can be added to further understand heat flow in mines. This study has also highlighted the importance of data sharing: a national or even global temperature database of the mined subsurface would allow for the uncertainties surrounding mine water thermal resources to be more easily addressed.  \n1. INTRODUCTION  \nAs one of the countries that signed both the 2015 Paris Agreement and 2021 Glasgow Climate Pact, the UK has committed itself tobe net zero by 2050, meaning efforts towards the green transition must be increasingly rapid, purposeful, and just. The energy required for space heating (and increasingly, space cooling) contributes to 42% of the UK's energy demand each year – more than is used to produce electricity (20%) or for transport (38%) . Approximately 90% of the required energy for space heating and cooling comes from the burning of natural gas (UK Government, 2021; Monaghan et al., 2022) . Therefore, finding alternative low-carbon solutions to fossil fuels to meet the demand for space heating and cooling is increasingly urgent. The UK Government outlined its UK Net Zero Strategy in 2021 which sets out a detailed economic plan for the country’s investment into the green transition, including £3.9 billion towards decarbonisi","cbCaig7heu0OdXS7","https://ap.wps.com/l/cbCaig7heu0OdXS7","pdf",896193,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why has machine learning been underused in UK mine water thermal applications?\",\"answer\":\"Because limited understanding of heat movement and behaviour in existing and abandoned mine systems has hindered development, despite machine learning’s ability to process large datasets.\"},{\"question\":\"What dataset and sources were used for the study?\",\"answer\":\"The study compiled publicly available temperature data from multiple UK organizations and added additional licensed Coal Authority data, forming a dataset with 2.4+ million datapoints across 800 locations.\"},{\"question\":\"What were the main modeling results for geothermal gradients and model quality?\",\"answer\":\"K-means clustering and multiple linear regression were used, and the preliminary models predicted geothermal gradients of 22°C/km (model A) and 23°C/km (models B and C) with R² values greater than 0.85.\"}]","Initial observations from machine learning approaches using UK temperature data for mine water thermal | 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has machine learning been underused in UK mine water thermal applications?","Question",{"text":75,"@type":76},"Because limited understanding of heat movement and behaviour in existing and abandoned mine systems has hindered development, despite machine learning’s ability to process large datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and sources were used for the study?",{"text":80,"@type":76},"The study compiled publicly available temperature data from multiple UK organizations and added additional licensed Coal Authority data, forming a dataset with 2.4+ million datapoints across 800 locations.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main modeling results for geothermal gradients and model quality?",{"text":84,"@type":76},"K-means clustering and multiple linear regression were used, and the preliminary models predicted geothermal gradients of 22°C/km (model A) and 23°C/km (models B and C) with R² values greater than 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