Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (1): 113-122.doi: 10.11959/j.issn.2096-3750.2022.00255

• Theory and Technology • Previous Articles     Next Articles

Research on water quality data classification based on weighted Naive Bayes

Zhihao FANG1, Zhengquan LI1,2, Mingwei ZHANG1   

  1. 1 School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
    2 HJiangsu Future Networks Innovation Institute, Nanjing 211111, China
  • Revised:2021-12-29 Online:2022-03-30 Published:2022-03-01
  • Supported by:
    The National Natural Science Foundation of China(61571108);The Wuxi Science and Technology Development Fund(H20191001);The Wuxi Science and Technology Development Fund(G20192010);The Future Network Scientific Research Fund Project(FNSRFP-2021-YB-11)

Abstract:

In order to better implement the water environmental management policies, water quality evaluation is the basic step, that is to reasonably divide it into specific water quality category according to multiple water quality parameters in a certain water area.Aimed at this problem, an improved Naive Bayes classification method was proposed, which endowed different attributes with different weights, weakened the assumption of Naive Bayes conditional independence, and made the classification result closer to the actual category.Firstly, referred to the data released by the national surface water quality automatic monitoring station, 500 water quality data were selected as samples, and an evaluation system with four indicators was established, including dissolved oxygen, permanganate index, ammonia nitrogen and total phosphorus.And then, the improved Naive Bayes classification method was used to learn and evaluate the samples, and its classification performance by the five fold cross validation method was verified.The results show that the accuracy, precision, recall and F1 value of the improved Naive Bayes classification method reach 96.0%, 95.9%, 93.8% and 94.8% respectively, with higher performance index of water quality data classification compared with other Naive Bayes classification method, which can provide some reference for the problem of water quality data classification encountered in actual engineering.

Key words: water quality evaluation, Naive Bayes, ive fold cross validation, performance index

CLC Number: 

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