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Indoor radon interval prediction in the Swedish building stock using machine learning
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology. Lund University, Sweden.ORCID iD: 0000-0002-2178-5391
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-8107-7768
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology.ORCID iD: 0000-0002-9860-4472
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-5044-6989
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2023 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 245, article id 110879Article in journal (Refereed) Published
Abstract [en]

Indoor radon represents a health hazard for occupants. However, the indoor radon measurement rate is low in Sweden because of no mandatory requirements. Measuring indoor radon on an urban scale is complicated, machine learning exploiting existing data for pattern identification provides a cost-efficient approach to estimate indoor radon exposure in the building stock. Extreme gradient boosting (XGBoost) models and deep neural network (DNN) models were developed based on indoor radon measurement records, property registers, and geogenic information. The XGBoost models showed promising results in predicting indoor radon intervals for different types of buildings with macro-F1 between 0.93 and 0.96, whereas the DNN models attained macro-F1 between 0.64 and 0.74. After that, the XGBoost models trained on the national indoor radon dataset were transferred to fit building registers in metropolitan regions to estimate the indoor radon intervals in non-measured and measured buildings by regions and building classes. By comparing the prediction results and the statistical summary of indoor radon intervals in measured buildings, the model uncertainty and validity were determined. The study ascertains the prediction performance of machine learning models in classifying indoor radon intervals and discusses the benefits and limitations of the data-driven approach. The research outcomes can assist preliminary large-scale indoor radon distribution estimation for relevant authorities and guide onsite measurements for prioritized building stock prone to indoor radon exposure. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 245, article id 110879
Keywords [en]
Sweden; Buildings; Forecasting; Health hazards; Learning systems; Neural network models; Radon; Uncertainty analysis; Building stocks; Deep learning; Exposure estimation; Indoor radon; Machine-learning; Predictive models; Radon exposure; Radon exposure estimation; Regional building stock; Xgboost; building; geogenic source; indoor radon; machine learning; prediction; Deep neural networks
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:ri:diva-67658DOI: 10.1016/j.buildenv.2023.110879Scopus ID: 2-s2.0-85172459457OAI: oai:DiVA.org:ri-67658DiVA, id: diva2:1815003
Note

This work has received funding from the Swedish Foundation for Strategic Research (SSF) [ FID18-0021 ] and the Maj and Hilding Brosenius Research Foundation .

Available from: 2023-11-27 Created: 2023-11-27 Last updated: 2024-02-14Bibliographically approved

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Wu, Pei-YuJohansson, TimSandels, ClaesMangold, MikaelMjörnell, Kristina

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