Intelligent air quality detection based on genetic algorithm and neural network: An urban China case study

Abstract

SummaryScientific and objective evaluations of atmospheric quality have become a primary task for researchers with the continuous development of modern industrial processes. At present, various approaches are used in monitoring air quality. The core factors of these approaches are the selection and establishment of an intelligent evaluation model. In this study, we designed a fuzzy genetic neural network model that fuses data based on the characteristics of autonomic learning and self‐organization and optimizes the fuzzy system by using the neural network model. A simulation was conducted to verify the feasibility of the algorithm. Results indicate that the proposed algorithm is not only a highly objective, scientific, and accurate method for detecting atmospheric environmental quality but also a practical solution.