Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis

− Air pollution is one of the most significant global challenges, with serious impacts on the health of living beings. In Indonesia, particularly in major cities such as Jakarta and Surabaya, the increase in the Air Quality Index (AQI) over the past few years indicates worsening air quality conditions. This decline in air quality is caused by increased industrial activities, motor vehicle emissions, and deforestation. Rising AQI levels pose severe health risks, including respiratory and cardiovascular diseases, and present major challenges for urban planning, public health management and environmental policy. Addressing this issue requires concerted efforts to implement sustainable practices, reduce emissions, and improve air quality management. The increasing air pollution level indicate the need for a more effective approach to identify and classify air quality index results with relevant success rates without using relatively expensive air quality index detection tools. This research aims to classify the air quality index using a Deep Neural Network model based on time-based feature expansion and spatial-temporal analysis. The Deep Neural Network model is used to extract complex patterns and hidden features in the data and help generate more accurate air pollution classifications. Meanwhile, time-based feature expansion is useful for extending the time representation in the data. The results of this research are expected to make a significant contribution in improving the global understanding of air pollution. By providing a cost-effective and efficient method for air quality monitoring, this study can lead to better pollution control measures. Furthermore, the insight gained from this research can help policymakers develop strategies to mitigate the adverse effects of air pollution on public health and the environment.