This project uses an LSTM neural network to predict air quality (PM2.5) from synthetic time-series data. It includes data generation, normalization, model training, and prediction visualization. The results demonstrate how deep learning can forecast pollution levels

Analysis of Air Pollution prediction and time-series forecast of PM2.5 Pollutant using Machine Learning Algorithms (SVM, Decision Tree and Random Forest) and Deep Learning Algorithms (CNN, Bi-LSTM). Also considered for improved performances is random search hyper-parameter tuning using Ray-Tune with HyperBand Scheduler strategy.

A major challenge involving pollution detection is to measure the average PM2.5 concentration over major metropolitan areas. One useful method for predicting PM2.5 over a relatively wide range are AOD measurements. Ensemble algorithms are more effective than linear algorithms for prediction. Pandas and scikit-learn were used for data analysis.

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prediction of pm2.5 pollution particles in chinese cities

Multivariate Time Series Forecasting with LSTMs in Keras

Advanced PM2.5 pollution prediction using interpretable tree-based ensembles with targeted feature engineering, variance reduction, and SHAP-driven model analysis. Includes systematic handling of imbalance, structural heteroscedasticity, and multicollinearity for robust and efficient forecasting.

Proyek ini merupakan proyek pengembangan model time series berbasis LSTM dan CNN untuk memprediksi kadar PM2.5 30 jam ke depan, sehingga dapat membantu memantau dan mengendalikan tingkat polusi udara demi melindungi kesehatan dan lingkungan.

Real-time air pollution analysis and prediction in London using LAQN data, with support for NO₂ and PM2.5 modeling from historical hourly observations.

Control self-made air purifier, read PM2.5/10 values indoors & outdoors and show air pollution predictions using Williamchart lib that I have contributed to.

A machine learning project to predict PM2.5 air pollution levels using PM10, NO₂, SO₂, and O₃ data. Built with Python, Flask, and RandomForestRegressor, it offers a REST API for real-time predictions. Ideal for smart cities, researchers, and air quality monitoring apps.

This repository presents an Air Quality Prediction system using IoT sensors and Deep Learning. It collects real-time environmental data (PM2.5, CO₂, temperature, humidity) via IoT devices and applies LSTM-based models for accurate forecasting, enabling smart city planning and pollution control.

This project predicts air quality (AQI and PM2.5) using a hybrid model of CatBoost and ARIMA with pollution and weather data. It includes preprocessing, feature engineering, and evaluation using RMSE and R². The model improves prediction accuracy and helps in efficient air quality monitoring and forecasting.

AQNet is an advanced air pollution Model system designed to predict PM2.5 levels using a hybrid deep learning model that combines LSTM, GRU, and Transformer architectures. It improves air quality predictions, enabling early warnings, data-driven policy decisions, and real-time deployment for smart cities and environmental safety.

Air-Pollution visualization and PM2.5 prediction with Python

Beijing air pollution PM2.5 prediction using LSTM neural networks.

Air pollution prediction using Intelligent Agents (CO, PM10, PM2.5)

Air pollution prediction using Intelligent Agents (CO, PM10, PM2.5)

Predicting PM2.5 pollution levels using machine learning involves using historical data on PM2.5 pollution and other related factors such as weather, traffic, and industrial activity to train a machine learning model. The trained model can then be used to predict future PM2.5 levels based on current or expected environmental conditions.

exploratory data analysis, visualization, and prediction on air pollution (PM2.5)

This projects use various data sources and structures to predict spatio-temporal pattern of air pollution in urban areas

Prediction of PM2.5 levels using weather and pollution data with Random Forest

Pollution prediction: Multivariate Linear Regression using Neural Networks on Beijing pm2.5 dataset

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Prediction of PM2.5 air pollution levels using machine learning and deep learning techniques with geospatial data analysis.

Correlation between Traffic and PM2.5 air pollution and prediction of the same using traffic

This Streamlit app predicts air pollution in a project area/region/province.

PM2.5 Air Pollution Prediction using Machine Learning & Time-Series Models (Python, Power BI, NAPS Data)

{A hybrid time series model for the spatio-temporal analysis of air pollution prediction based on PM2.5}

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