Research on groundwater impurity identification based on big data fusion
huiwen wu, xiaoping huang · 2024
With the acceleration of urbanization, the quality of groundwater has been seriously threatened, and impurity identification has become an urgent problem to be solved. Based on big data fusion technology, this study uses machine learning and deep learning algorithms to intelligently identify groundwater impurities. Firstly, groundwater quality data, including turbidity, total suspended matter, pH value, dissolved oxygen and other indicators, were collected, and pretreatment and feature extraction were carried out. Then, support vector machine, random forest and neural network algorithms are used to build a classification model to classify and predict the impurities in groundwater. The experimental results show that the groundwater impurity identification method based on big data fusion has high accuracy and stability, and can provide strong support for groundwater quality monitoring and treatment. This study not only enriched the theoretical system of groundwater impurity identification, but also provided an operable scheme for practical application.