IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i8p857-d106342.html
   My bibliography  Save this article

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

Author

Listed:
  • Zhijian Liu

    (Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
    These authors contributed equally.)

  • Hao Li

    (Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
    Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
    These authors contributed equally.)

  • Guoqing Cao

    (Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China)

Abstract

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM 2.5 and PM 10 ), temperature, relative humidity, and CO 2 concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups.

Suggested Citation

  • Zhijian Liu & Hao Li & Guoqing Cao, 2017. "Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning," IJERPH, MDPI, vol. 14(8), pages 1-9, July.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:8:p:857-:d:106342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/8/857/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/14/8/857/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    2. Thanh-Dong Pham & Byeong-Kyu Lee, 2014. "Feasibility of Silver Doped TiO 2 /Glass Fiber Photocatalyst under Visible Irradiation as an Indoor Air Germicide," IJERPH, MDPI, vol. 11(3), pages 1-18, March.
    3. Zhijian Liu & Kejun Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
    4. Kalogirou, Soteris A & Panteliou, Sofia & Dentsoras, Argiris, 1999. "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater," Renewable Energy, Elsevier, vol. 18(1), pages 87-99.
    5. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    6. Mathieu Lanthier-Veilleux & Geneviève Baron & Mélissa Généreux, 2016. "Respiratory Diseases in University Students Associated with Exposure to Residential Dampness or Mold," IJERPH, MDPI, vol. 13(11), pages 1-12, November.
    7. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    8. Senthaamarai Rogawansamy & Sharyn Gaskin & Michael Taylor & Dino Pisaniello, 2015. "An Evaluation of Antifungal Agents for the Treatment of Fungal Contamination in Indoor Air Environments," IJERPH, MDPI, vol. 12(6), pages 1-14, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Piotr Boniecki & Małgorzata Idzior-Haufa & Agnieszka A. Pilarska & Krzysztof Pilarski & Alicja Kolasa-Wiecek, 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm," IJERPH, MDPI, vol. 16(18), pages 1-9, September.
    2. Anyu Yu & Guangshe Jia & Jianxin You & Puwei Zhang, 2018. "Estimation of PM 2.5 Concentration Efficiency and Potential Public Mortality Reduction in Urban China," IJERPH, MDPI, vol. 15(3), pages 1-19, March.
    3. Wenxing Wang & Guoqi Dang & Imran Khan & Xiaobin Ye & Lei Liu & Ruqing Zhong & Liang Chen & Teng Ma & Hongfu Zhang, 2022. "Bacterial Community Characteristics Shaped by Artificial Environmental PM2.5 Control in Intensive Broiler Houses," IJERPH, MDPI, vol. 20(1), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    3. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    4. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    5. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
    6. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
    7. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
    8. Mao, Chunliu & Li, Muran & Li, Na & Shan, Ming & Yang, Xudong, 2019. "Mathematical model development and optimal design of the horizontal all-glass evacuated tube solar collectors integrated with bottom mirror reflectors for solar energy harvesting," Applied Energy, Elsevier, vol. 238(C), pages 54-68.
    9. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    10. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    11. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    12. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    13. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    14. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
    15. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
    16. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    17. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    18. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    19. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    20. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:14:y:2017:i:8:p:857-:d:106342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.