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Research on Data Cleaning Algorithm Based on Multi Type Construction Waste

Author

Listed:
  • Pengfei Wang

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No.15 Yongyuan Rd., Daxing District, Beijing 102616, China)

  • Yang Liu

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No.15 Yongyuan Rd., Daxing District, Beijing 102616, China
    Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, No.1 Zhanlanguan Rd., Xicheng District, Beijing 100044, China
    Beijing Key Laboratory of Urban Spatial Information Engineering, No.15 Yangfangdian Rd., Haidian District, Beijing 100038, China)

  • Qinqin Sun

    (Beijing Key Laboratory of Urban Spatial Information Engineering, No.15 Yangfangdian Rd., Haidian District, Beijing 100038, China)

  • Yingqi Bai

    (Beijing Key Laboratory of Urban Spatial Information Engineering, No.15 Yangfangdian Rd., Haidian District, Beijing 100038, China)

  • Chaopeng Li

    (Beijing Key Laboratory of Urban Spatial Information Engineering, No.15 Yangfangdian Rd., Haidian District, Beijing 100038, China)

Abstract

Owing to urbanization, the output of construction waste is increasing yearly. Garbage treatment plays a vital role in urban development and construction. The accuracy and integrity of data are important for the implementation of construction waste treatment. Abnormal detection and incomplete filling occur when traditional cleaning algorithms are used. To improve the cleaning of construction waste data, a data cleaning algorithm based on multi-type construction waste was presented in this study. First, a multi-algorithm constraint model was designed to achieve accurate matching between the cleaning content and cleaning model. Thereafter, a natural language data cleaning model was proposed, and the spatial location data were separated from the general data through the content separation mechanism to effectively frame the area to be cleaned. Finally, a time series data cleaning model was constructed. By integrating “check” and “fill”, large-span and large-capacity time series data cleaning was realized. This algorithm was applied to the data collected by the pilot cities, which had precision and recall rates of 93.87% and 97.90% respectively, compared with the traditional algorithm, ultimately exhibiting a certain progressiveness. The algorithm proposed herein can be applied to urban environmental governance. Furthermore, this algorithm can markedly improve the control ability and work efficiency of construction waste treatment, and reduce the restriction of construction waste on the sustainable development of urban environments.

Suggested Citation

  • Pengfei Wang & Yang Liu & Qinqin Sun & Yingqi Bai & Chaopeng Li, 2022. "Research on Data Cleaning Algorithm Based on Multi Type Construction Waste," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12286-:d:926977
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    References listed on IDEAS

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    1. Ahmed M. A. Shohda & Mahrous A. M. Ali & Gaofeng Ren & Jong-Gwan Kim & Ahmed M. Abdo & Wael R. Abdellah & Abbas M. Hassan, 2022. "Sustainable Assignment of Egyptian Ornamental Stones for Interior and Exterior Building Finishes Using the AHP-TOPSIS Technique," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    2. Jan Polcyn, 2022. "Determining Value Added Intellectual Capital (VAIC) Using the TOPSIS-CRITIC Method in Small and Medium-Sized Farms in Selected European Countries," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    3. Olamide Jogunola & Bamidele Adebisi & Khoa Van Hoang & Yakubu Tsado & Segun I. Popoola & Mohammad Hammoudeh & Raheel Nawaz, 2022. "CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-16, January.
    4. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).
    5. Bo Zeng & Yuxiang Sun & Shaojun Xie, 2022. "Application of LSTM algorithm combined with Kalman filter and SOGI in phase-locked technology of aviation variable frequency power supply," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-16, April.
    6. Jae-Sang Hwang & Sung-Duk Mun & Tae-Joon Kim & Geun-Won Oh & Yeon-Sub Sim & Seung Jin Chang, 2022. "Development of Data Cleaning and Integration Algorithm for Asset Management of Power System," Energies, MDPI, vol. 15(5), pages 1-18, February.
    7. Cui-Bin Ji & Gui-Jiang Duan & Jun-Yan Zhou & Wei-Jie Xuan & Xianming Zhang, 2021. "Equipment Quality Data Integration and Cleaning Based on Multiterminal Collaboration," Complexity, Hindawi, vol. 2021, pages 1-16, October.
    8. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).
    9. Jin Sol Hwang & Jung-Su Kim & Hwachang Song, 2022. "Handling Load Uncertainty during On-Peak Time via Dual ESS and LSTM with Load Data Augmentation," Energies, MDPI, vol. 15(9), pages 1-20, April.
    10. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    11. Vaishnudebi Dutta & Subhomoy Haldar & Prabjot Kaur & Yuvraj Gajpal & Qingyuan Zhu, 2022. "Comparative Analysis of TOPSIS and TODIM for the Performance Evaluation of Foreign Players in Indian Premier League," Complexity, Hindawi, vol. 2022, pages 1-20, April.
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    Cited by:

    1. Katerina Zdravkova, 2023. "Personalized Education for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-13, April.

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