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Indoor radon interv...
Indoor radon interval prediction in the Swedish building stock using machine learning
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- Wu, Pei-Yu (författare)
- Lunds universitet,RISE,Mätteknik,Lund University, Sweden,Avdelningen för Byggnadsfysik,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,Division of Building Physics,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH,Research Institutes of Sweden (RISE)
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- Johansson, Tim (författare)
- RISE,Systemomställning och tjänsteinnovation,Research Institutes of Sweden (RISE)
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- Sandels, Claes, 1985- (författare)
- RISE,Mätteknik,Research Institutes of Sweden (RISE)
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- Mangold, Mikael (författare)
- RISE,Systemomställning och tjänsteinnovation,Research Institutes of Sweden (RISE)
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- Mjörnell, Kristina (författare)
- Lunds universitet,RISE,Lund University, Sweden,Avdelningen för Byggnadsfysik,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: Cirkulär byggindustri,LTH profilområden,Division of Building Physics,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Circular Building Sector,LTH Profile areas,Faculty of Engineering, LTH,Research Institutes of Sweden (RISE)
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(creator_code:org_t)
- Elsevier Ltd, 2023
- 2023
- Engelska.
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Ingår i: Building and Environment. - : Elsevier Ltd. - 0360-1323 .- 1873-684X. ; 245
- Relaterad länk:
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https://doi.org/10.1...
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http://dx.doi.org/10... (free)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Husbyggnad (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Building Technologies (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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