Revolutionizing Air Quality Prognostication: Fusion of Deep Learning and Density-Based Spatial Clustering of Applications with Noise for Enhanced Pollution Prediction

Aim: This endeavor focuses on using deep learning and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to improve pollution prediction in order to alter air quality prediction. Materials and Methods: Three different groups are working together on this endeavor. Group 1 refers to Multi-Layer Perceptron (MLP) to attain accuracy. Group 2 refers to Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to achieve the same goal. Results: Each system module's unique outputs, such as raw and pre-processed air quality data, chosen features, trained Support Vector Machine or Logistic Regression models, and forecasts with an accuracy rate of 81% are described in depth in the output design. Conclusion: Using the DBSCAN algorithm, the proposed air pollution monitoring system provides an effective data-driven method for locating pollution hotspots, trends, and well-informed decision-making.