Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

Simplification of Water Quality Classification in Beijing, Tianjin and Hebei Based on K-nearest Neighbor Algorithm

Authors
ZIYI ZHU1, *
1Statistics and Mathematics institute, Central University of Finance and Economics, Yunnan, 102206 (zhuziyi0904@163.com)
Corresponding Author
ZIYI ZHU
Available Online 15 December 2021.
DOI
https://doi.org/10.2991/assehr.k.211209.556How to use a DOI?
Keywords
k-nearest neighbor; water quality classification; water quality predict
Abstract

This paper repeated experiments for many times, and simplified the original five water quality categories into two categories, based on the samples of water quality monitoring in Beijing Tianjin Hebei Haihe River Basin. Based on water quality classification, the three most obvious variables of stratification: turbidity, total phosphorus and total nitrogen are selected. The k-nearest neighbor method is used to predict the classification results of the test set, and the accuracy is about 75%.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
Series
Advances in Economics, Business and Management Research
Publication Date
15 December 2021
ISBN
978-94-6239-483-4
ISSN
2352-5428
DOI
https://doi.org/10.2991/assehr.k.211209.556How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - ZIYI ZHU
PY  - 2021
DA  - 2021/12/15
TI  - Simplification of Water Quality Classification in Beijing, Tianjin and Hebei Based on K-nearest Neighbor Algorithm
BT  - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
PB  - Atlantis Press
SP  - 3423
EP  - 3426
SN  - 2352-5428
UR  - https://doi.org/10.2991/assehr.k.211209.556
DO  - https://doi.org/10.2991/assehr.k.211209.556
ID  - ZHU2021
ER  -