AQIPred: A Hybrid Model for High Precision Time Specific Forecasting of Air Quality Index with Cluster Analysis

Abstract

Abstract The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. Especially to predict wind direction, air and water quality, and flooding. In the context of doing this research, an MLP-LSTM Hybrid Model was developed to be able to generate predictions of this nature. An investigation into the Beijing Multi-Site Air-Quality Data Set was carried out in the context of an experiment. In this particular scenario, the model generated MSE values that came in at 0.00016, MAE values that came in at 0.00746, RMSE values that came in at 13.45, MAPE values that came in at 0.42, and R 2 values that came in at 0.95. This is an indication that the model is functioning effectively. The conventional modeling techniques for forecasting, do not give the level of performance that is required. On the other hand, the results of this study will be useful for any type of time-specific forecasting prediction that requires a high level of accuracy.

Keywords

• Modeling
• Prediction
• Time series forecasting
• Neural network