LSTM Derin Öğrenme Yaklaşımı ile Hava Kalitesi Verilerinin Tahmini: Sakarya Örneği

In recent years, air quality forecasting and taking preventive and corrective measures have become important as many urban areas worldwide are exposed to severe air pollution and the health hazards that come with it.In this study, air quality estimation was made with machine learning, considering the air quality parameters monitored at air quality stations in Sakarya province.For this purpose, the Long Short-Term Memory (LSTM) deep learning approach was applied to estimate PM10, CO, NOX, and NO2 concentrations from air quality parameters, and performance results were obtained.As a result, it has been seen that the applied method has good estimation performance.