Intelligent recognition technology of heavy metal pollution based on deep learning and fuzzy clustering and its application
Yuhan Xu, Yongtao Wang · 2021
In the process of anomaly identification of polluted soil, in order to improve the analysis and interpretation ability of inversion results of multi-electrode resistivity method, and overcome a series of shortcomings of manual interpretation, such as unable to guarantee the interpretation quality, this paper combines deep learning, fuzzy c-means clustering and hypothesis testing technology to form a set of intelligent detection and identification technology suitable for various types of pollution. From the experimental results of the numerical model and the processing results of the measured data, it can be known that this method can complete the intelligent identification of contaminated soil effectively.