_eucys2022-project-title-performance-of-machine-learning-algorithms-for-predicting-air-pollution-parameters.jpg "EUCYS2022 Project Title - Performance of machine learning algorithms for predicting air pollution parameters from forecasting models and methods Video Screenshot Preview")
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⏲ Duration: 1 min 16 sec
✓ Published: 28-Jul-2022
✓ Published: 28-Jul-2022
Description:
One of the world's most critical challenges we face today is air pollution, generating numerous health issues and negatively impacting the environment. This study compares machine learning models like LSTM, ARIMA, SARIMAX, BVAR, VAR, GRU, and Prophet for 24 hours predictions of NO2 and PM2.5 concentrations. The project's goal was to test the performance of machine learning algorithms and develop a methodology for identifying the most accurate one aiming to integrate it with urban air monitoring
One of the world's most critical challenges we face today is air pollution, generating numerous health issues and negatively impacting the environment. This study compares machine learning models like LSTM, ARIMA, SARIMAX, BVAR, VAR, GRU, and Prophet for 24 hours predictions of NO2 and PM2.5 concentrations. The project's goal was to test the performance of machine learning algorithms and develop a methodology for identifying the most accurate one aiming to integrate it with urban air monitoring
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