Site pollution characteristic factor identification and monitoring index optimization method based on data mining

Abstract

The invention discloses a site pollution characteristic factor identification and monitoring index optimization method based on data mining, and the method comprises the steps: firstly carrying out the preprocessing of underground water pollution data of a polluted site, which is obtained in advance; secondly, constructing a self-organizing mapping neural network model, and carrying out a series of data mining tasks such as data dimension reduction, correlation analysis and visual analysis after model training is completed; secondly, further performing unsupervised clustering learning on the self-organizing mapping classification result by adopting a K-means algorithm to achieve identification of characteristic factors; and finally, adopting a strategy of firstly classifying and then grading the pollution indexes, and optimizing the later monitoring process of the indexes. Technical support can be provided for polluted site data monitoring, data analysis and decision-making management,site pollution characteristic factors are identified through data mining of a polluted site, meanwhile, monitoring indexes are optimized, and finally the purpose of reducing site monitoring cost is achieved.

Description

technical field [0001] The invention belongs to the field of groundwater environment science and technology, and in particular relates to a method for identifying characteristic factors of site pollution and optimizing monitoring indicators based on data mining. Background technique [0002] Due to the adjustment of my country's industrial structure, a large number of enterprises have been shut down or relocated, leaving behind a large number of industrially polluted sites. These industrially polluted sites often have the characteristics of heavy pollution, complex pollutant composition, and polluted soil and groundwater. , drinking water safety, ecological environment, healthy living environment, and sustainable economic and social development have posed serious threats and challenges. Therefore, it is urgent to carry out investigation and evaluation, risk control and restoration of contaminated sites. [0003] In the preliminary investigation and evaluation of contaminated...

AI Technical Summary

Problems solved by technology

However, with the improvement of data collection capabilities, the data for polluted sites is not only large in volume, but also complex in data structure, which hides a large amount of characteristic information, relational information, and classification information. At the same time, the pollution data is not only random, but also shows strong Non-linear, it is difficult to directly use traditional data analysis methods to analyze these multidimensional data, and it is also difficult to directly understand the data structure and obtain useful information from multidimensional data sets

In addition, there is currently no technical method to optimize the monitoring of site pollutant indicators, so as to reduce the cost of site monitoring

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