An innovative decision making method for air quality monitoring based on big data-assisted artificial intelligence technique

Abstract

This work dissects the application of big data and artificial intelligence (AI) technology in environmental protection monitoring. The application principle of big data in environmental data collection is analysed based on atmospheric science and AI technology. In addition, a combined model of air quality forecasting based on machine learning is proposed to resolve real air quality monitoring challenges in environmental protection, namely, the improved complete ensemble empirical mode decomposition with adaptive noise-whale optimization algorithm-extreme learning machine (ICEEMDAN-WOA-ELM). On this basis, deep learning is introduced to establish a deep learning-based time-space-type-meteorology (TSTM) model to predict air quality. Finally, the model is verified by experiments. The results demonstrate that the ICEEMDAN-WOA-ELM model significantly outperforms a single AI model in air quality forecasting. The five evaluation index values of ICEEMDAN-WOA-ELM are 14.187, 17.235, 0.140, 0.067, and 0.946, which are higher than those of the other models. The single-step accuracy and average of the TSTM model in the heavily polluted weather forecast results almost reached full marks, with a maximum of 1.00. The performance also decreases with the growth of the step size but remains above 0.86. It can be seen that a single AI model can no longer meet the requirements of air quality forecasting. The ICEEMDAN-WOA-ELM model combined with big data has advantages in air quality monitoring and is effective for environmental protection.