Preprocessing method for automatic water quality monitoring data

An automatic monitoring and preprocessing technology, which is applied in the direction of electrical digital data processing, special data processing applications, digital data information retrieval, etc., can solve the problems of large amount of data, preprocessing of difficult data analysis methods, high frequency of automatic monitoring data, etc., to achieve The effect of high reliability, ease of subsequent analysis and application

Pending Publication Date: 2021-12-07
TIANJIN UNIV
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Problems solved by technology

However, due to the characteristics of high frequency and large data volume of automatic monitoring data, it is difficult to use conventional visual inspection or simple data a

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  • Preprocessing method for automatic water quality monitoring data
  • Preprocessing method for automatic water quality monitoring data
  • Preprocessing method for automatic water quality monitoring data

Examples

Experimental program
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[0056] Example 1

[0057] 1. Data introduction

[0058] The data set in this example is the online monitoring data of water quality at the monitoring point of the Taolinkou Reservoir water source station in Qinhuangdao City, Hebei Province from 16:00 on August 24, 2016 to 16:00 on April 19, 2018. The data collection frequency is 4h / time, with a total of 14,476 records, including four common water quality indicators, including pH value, temperature (Temp), chlorophyll (CHL), and dissolved oxygen (DO). Figure 2-5 shown.

[0059] 2. Count missing variables, number of values ​​and visualize them

[0060] A complete time series was constructed for the water quality data, and the missing values ​​were marked as "NA". The number of missing values ​​for each water quality variable is shown in Table 1. Visualize missing values ​​using missingno package in python, such as Image 6 As shown, the white part is the missing value of each water quality variable.

[0061] Table 1 Statis...

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Abstract

The invention provides a preprocessing method for automatic water quality monitoring data. The preprocessing method comprises the following steps: S1, counting and marking missing values in an original data set; S2, performing time sequence decomposition on the data in the original data set to obtain a residual term; S3, identifying an abnormal value in the residual item, and deleting and/or correcting the abnormal value; and S4, interpolating missing values and deleted abnormal values in the original data set by adopting a linear interpolation method to obtain a processed complete data set. Compared with the prior art, the preprocessing method has the advantages that abnormal values and missing values in the automatic water quality monitoring value set are effectively processed, and a complete and high-reliability data set can be obtained, so that convenience is provided for subsequent analysis and application of automatic water quality monitoring data.

Description

technical field [0001] The invention relates to the technical field of water quality data preprocessing, in particular to a method for preprocessing water quality automatic monitoring data. Background technique [0002] Using historical water quality monitoring data to evaluate and predict water quality is an important part of water environment management. The traditional water quality monitoring method is mainly manual monitoring with monthly or quarterly monitoring intervals. In recent years, with the advent of the era of big data, automatic water quality monitoring stations have gradually attracted the attention of researchers. Different from the water quality data of conventional manual monitoring, the automatic monitoring data obtained by the automatic water quality monitoring station has the characteristics of high frequency and large amount of data, which can more comprehensively and accurately identify the spatiotemporal change law of water quality variables and the ...

Claims

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Application Information

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IPC IPC(8): G06F16/215G06F16/2458
CPCG06F16/215G06F16/2474
Inventor 郝玉莹赵林孙同傅少康孙梦胡斯宇
Owner TIANJIN UNIV
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