Article,
Design and Analysis of Air Pollution Concentration Prediction Models Using Transfer Learning and Recurrent Neural Networks
CENTRAL ASIAN JOURNAL OF THEORETICAL AND APPLIED SCIENCE, 4 (8): 135-142 (August 2023)
Abstract
Since air pollution (AP) poses a serious risk to human health, many people have started paying greater attention to it in recent years. Precise Air Pollution prediction helps individuals schedule their outdoor activities and contributes to human health protection. In this study, recurrent neural networks (RNNs) with long short-term memory (LSTM) were used to predict Macau's future APS concentration. Data on the concentration of APS as well as environmental data have also been used. Additionally, some air quality monitoring stations (AQMSs) in Macau have fewer overall observed data while simultaneously collecting less observed data for specific APS kinds. In order to help AQMSs with less observed data, transfer learning, and pre-trained neural networks have been utilized. The purpose of this study is to show how a collection of neural network algorithms has been utilized for these two pollutant elements. The approach is given considerable thought in this paper, and datasets regarding air and water pollution as well as expected parameters were additionally collected for future development efficiency.
