[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124314-en":3,"doc-seo-124314-105":30,"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":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},124314,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Interpretability in Machine Learning for IAQ and HVAC Optimisation - A Response to Oka et al","Advanced deep learning methods are used to improve indoor air quality (IAQ) and support energy-efficient HVAC operation in buildings. The study responds to key challenges in model design, bias mitigation, validation rigor, and practical implementation for real educational environments. It combines architecture optimization (weights, hyperparameters, hidden-layer configuration), G-DeepSHAP-based feature-importance interpretation with CNN-assisted visualization, and k-fold cross-validation plus sensitivity analysis. Empirical validation and systematic refinement address limitations of traditional air-pollution analysis and strengthen interpretability for stakeholder trust.","The University of Manchester Research  \nInterpretability in Machine Learning for IAQ and HVAC Optimisation  \nDocument Version  \nProof  \nLink to publication record in Manchester Research Explorer  \nCitation for published version (APA):  \nEjohwomu, O. (2025) . Interpretability in Machine Learning for IAQ and HVAC Optimisation: A Response to Oka et  \nal. Building and Environment, 1-12 . [https://doi.org/10.1016/j.buildenv.2025.113494](https://doi.org/10.1016/j.buildenv.2025.113494)  \nPublished in:  \nBuilding and Environment  \nCiting this paper  \nPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTakedown policy  \nIf you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [[http://man.ac.uk/04Y6Bo](http://man.ac.uk/04Y6Bo)] or [contact openresearch@manchester.ac.uk](contact openresearch@manchester.ac.uk) providing relevant details, so  \nwe can investigate your claim.  \nDownload date:05 . Aug. 2025  \nJournal Pre-proof  \nInterpretability in Machine Learning for IAQ and HVAC Optimisation: A Response to Oka et al  \nSeyed Hamed Godasiaei , Obuks A. Ejohwomu , Hua Zhong , Douglas Booker  \nPII: S0360-1323(25)00967-9  \nDOI: [https://doi.org/10.1016/j.buildenv.2025.113494](https://doi.org/10.1016/j.buildenv.2025.113494)  \nReference: BAE 113494  \nTo appear in: Building and Environment  \nReceived date: 25 June 2025  \nAccepted date: 27 July 2025  \nPlease cite this article as: Seyed Hamed Godasiaei , Obuks A. Ejohwomu , Hua Zhong , Douglas Booker , Interpretability in Machine Learning for IAQ and HVAC Optimisation: A Response to Oka et al, Building and Environment (2025), doi: [https://doi.org/10.1016/j.buildenv.2025.113494](https://doi.org/10.1016/j.buildenv.2025.113494)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 Published by Elsevier Ltd.  \nHighlights  \n• ML models ensure energy savings and improved air quality in schools.  \n• Model optimisation via weights, hyperparameters & layers boosts accuracy.  \n• k-fold cross-validation & sensitivity tests ensure robust results.  \n• Optimised models & experimental validation beat traditional pollution limits.  \n• Self-supervised models enable adaptive IAQ management in buildings.  \n• G-DeepSHAP reduces bias in feature importance interpretation.  \nInterpretability in Machine Learning for IAQ and HVAC Optimisation: A Response to Oka et al.  \nSeyed Hamed Godasiaeia, ObuksA. Ejohwomub, c, Hua Zhongd, Douglas BookereaSchool of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, China bDepartment of Mechanical, Aerospace and Civil Engineering, University of Manchester, Engineering Building A, Booth Street E, Manchester, M13 9PL, United Kingdom  \ncCIDB Centre of Excellence, University of Johannesburg, Johannesburg 2092, South Africa dSchool of Built Environment and Architecture, London South Bank University, United KingdomeSchool of Civil Engineering, University of Leeds, United Kingdom  \n*Corre","cbCaipdTlmHhV2qq","https://ap.wps.com/l/cbCaipdTlmHhV2qq","pdf",921652,1,13,"English","en",105,"# Highlights\n# Abstract\n# Keywords\n# Introduction\n## Energy efficiency and IAQ challenges in educational environments","[{\"question\":\"What deep learning approaches are discussed for capturing air-pollution patterns?\",\"answer\":\"The work highlights advanced deep learning models including GRUs, RNNs, LSTMs, and CNNs to capture temporal and spatial patterns in air pollution data.\"},{\"question\":\"How does the paper address interpretability and bias in feature importance?\",\"answer\":\"It uses G-DeepSHAP to reduce bias in interpretation of feature importance and complements it with CNN-assisted visualization methods.\"},{\"question\":\"How are model robustness and validation ensured?\",\"answer\":\"Robustness is supported through k-fold cross-validation and sensitivity analysis, combined with empirical validation and systematic model refinement.\"}]","Interpretability in Machine Learning for IAQ and HVAC Optimisation - A Response to Oka et al | PDF",1785821548,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"interpretability-in-machine-learning-for-iaq-and-hvac-optimisation-a-response-to-oka-et-al","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretability-in-machine-learning-for-iaq-and-hvac-optimisation-a-response-to-oka-et-al/124314/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",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},"What deep learning approaches are discussed for capturing air-pollution patterns?","Question",{"text":76,"@type":77},"The work highlights advanced deep learning models including GRUs, RNNs, LSTMs, and CNNs to capture temporal and spatial patterns in air pollution data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper address interpretability and bias in feature importance?",{"text":81,"@type":77},"It uses G-DeepSHAP to reduce bias in interpretation of feature importance and complements it with CNN-assisted visualization methods.",{"name":83,"@type":74,"acceptedAnswer":84},"How are model robustness and validation ensured?",{"text":85,"@type":77},"Robustness is supported through k-fold cross-validation and sensitivity analysis, combined with empirical validation and systematic model refinement.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]