Research on Data Cleaning Algorithm Based on Multi Type Construction Waste

Pengfei Wang (), Yang Liu, Qinqin Sun, Yingqi Bai and Chaopeng Li
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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
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

Sustainability, 2022, vol. 14, issue 19, 1-16

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.

Keywords: multi type data; construction waste; data cleaning; multi algorithm constraint model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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