Deep Learning-based Air Quality Forecasting System for Urban India Using Temporal Neural Networks
TL;DR
It can be concluded that CNN-LSTM is more effective than other systems and has some advantages over single-LSTM and the transformer, and also has some advantages over single-LSTM and the transformer.
Abstract
Air pollution poses a serious hazard to public health in Cities of India; Among all sources, those resulting from Energy Consumption dominate the deaths caused by air pollution. Economic growth has intensified air pollution and climate change issues at the same time. Existing prediction methods focus on isolated single-site time-series forecasting, ignoring spatial dependencies and cross-city pollution transport, limiting regional management effectiveness. Based on the hourly air quality data of key pollutants in seven cities across India during 2015-2020, this paper first explores their concentrations and correlations; Then it builds a Deep Learning forecasting system consisting of Long Short-Term Memory (LSTM), a convolutional neural network, and long short-term memory networks (CNN-LSTM) and a transformer to investigate its AQI-prediction performance. LSTM can capture long-term temporal dependencies; CNN-LSTM combines spatial-temporal information model integration; Transformers explore long-range relationships through attention mechanisms. Based on the experimental results of this paper, it can be concluded that CNN-LSTM is more effective than other systems. It also has some advantages over single-LSTM and the transformer.