There are 2 repositories under air-pollution-prediction topic.
Time Series Analysis of Air Pollutants(PM2.5) using LSTM model
Welcome to quote our published papers, and the codes have been uploaded.
In this project work, the main motive is to build a deep learning model to detect air pollution from real-time images. In order to achieve that goal, we have collected data from different sources and then enhanced the low-quality images using the Image enhancement technique. Our next step was to train a CNN (Convolutional Neural Network) on the images in order to detect air pollution by analyzing the clearness of the sky in the image. In this work, we have used the Inception V3 model. After the successful testing of the CNN model, we have deployed the model on an Android Application.
The present project aims to predict air pollution in Beijing, China, using the data set "Beijing Multi-Site Air-Quality Data Data Set"
Time series analysis to predict AQI in Delhi, dataset collated from Indian Meteorological department of India.
Air pollution prediction using linear regression
This problem statement focused on building machine learning models that would assist create city-level air pollution susceptibility maps with a 5-meter spatial resolution for milan city in Italy. This city has unique challenges of dealing with pollution levels due to its unique topographic features.
Forecasting temperature and air pollutants like CO, NO2, SO2, O3, PM2.5, PM10 using different ML & DL models.