Prediction of Air Pollution LSTM Model Use in Machine Learning
Keywords:
LSTM (Long Short-Term Memory), Time series data, Air pollution forecast, Air Quality Index (AQI), Recurrent neural networks (RNNs)Abstract
A major environmental problem that has an impact on the health and happiness of millions of people is air pollution. Accurate air quality forecasting is crucial for maintaining human health, the environment, and urban planning. In recent years, machine learning algorithms, such as Long Short-Term Memory (LSTM) models, have been increasingly used to predict air pollution levels. This study presents an abstract for a system that uses an LSTM model for air pollution prediction. The proposed system will collect data from various sources, pre-process the data, train an LSTM model, predict the air quality index (AQI), and alert users when the predicted AQI exceeds a certain threshold level. The system is designed to be scalable, reliable, secure, user-friendly, and compatible with different types of operating systems. This study highlights the importance of air pollution prediction and proposes a solution using an LSTM model that can contribute to improving public health and environmental protection.
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