Air Pollution Prediction using Multivariate LSTM Deep Learning Model
Keywords:
Air Pollution Prediction, Deep Learning, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Time SeriesAbstract
Air pollution prediction is the process of using data analysis and modelling techniques to forecast the level of pollutants in the air at a future time or location. Air pollution prediction using deep learning is an active area of research and has many practical applications, including improving public health, reducing environmental damage, and supporting decision-making processes for urban planning and transportation management. This paper presents a Long Short-Term Memory (LSTM) based air pollution prediction model. LSTM is a type of Recurrent Neural Network (RNN) that can be used to predict air pollution levels. LSTM models are particularly useful for predicting time series data, such as air pollution levels measured at specific time intervals. LSTM models can be used to predict air pollution levels by learning complex patterns in the historical data and identifying the factors that contribute to high levels of pollution.
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