Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Abstract

Water pollution prevention and control is crucial to ensure the safety of water environment and human health, and various types of algorithms play an important role in it. We introduce the history and algorithm overview of various algorithms in water pollution prevention and control, analyze the current research status and recent research results in this field, compare and evaluate the advantages and disadvantages of various algorithms, and focus on the following algorithms: neural network, convolutional neural network, decision tree, random forest, naive Bayes, SVM, K-Means, and AdaBoost. Through the comparative analysis of these algorithms, we hope to provide a more effective method for water pollution prevention and control.