Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

:octocat: Detection and Prediction of Air quality Index :octocat:

Forecasting air pollution using temporal attention mechanism in Beijing

☁Air pollution visualization and forecasting platform based on Spring Cloud(基于Spring Cloud的环境污染物数据分析与预测平台)

Stacking a machine learning ensemble for multivariate time series forecasting, with the goal of predicting the one-period ahead PM 2.5 air pollution level, as published in Towards Data Science on Medium.com

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

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Forecasting air pollution using ESA Sentinel-5p data, and an encoder-decoder convolutional LSTM neural network architecture, implemented in Pytorch

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☁Air pollution visualization and forecasting platform based on Flask and TensorFlow

Deep learning air-pollution forecaster with Pytorch

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.

This application provides current, forecast and historical air pollution data for majority countries. You can check the pollution index of your country by simply scrolling or searching for it using the search bar and clicking on it to show more details. This application is built with React.js and Redux.

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Air pollution forecasting with convection-diffusion model and LSTM

The official implementation of paper "MADGCN: A Meteorology-Aware Spatio-Temporal Graph Convolution Netowrk for Long-term Air Pollution Forecasting"

Generally, Air pollution refers to the release of pollutants into the air that are detrimental to human health and the planet as a whole. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Hence, air quality evaluation and prediction has become an important research area. The aim is to investigate machine learning based techniques for air quality forecasting by prediction results in best accuracy. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments and analyse the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of air quality pollution by accuracy calculation. To propose a machine learning-based method to accurately predict the Air Quality Index value by prediction results in the form of best accuracy from comparing supervise classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given transport traffic department dataset with evaluation of GUI based user interface air quality prediction by attributes.

The goal was to use Neural Networks to predict future values from historical data in specific locations. In the context of this master thesis, 6 research papers were studied for the different types of neural network models on data related to air pollution and time series forecasting.

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among the scientific community. The main causes associated with air pollution are the burning of fossil fuels, agriculture, exhaust from factories and industries, residential heating, and natural disasters. The Environmental Protection Agency (EPA) tracks the pollution level by calculating the amount of ground-level ozone (O3), Sulphur dioxide (SO2), particulates matter (PM10 and PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) present in the air molecule. These substances are in compositions of a common index, called the Air Quality Index (AQI), indicating how clean or polluted the air is currently or forecasted to become in areas. This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The information fetched from the cloud is transmitted to the Machine learning models which contains the detailed dataset for the pollutants and these models accurately predict the day-wise pollutant concentrations and display them using an application. This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm and K-nearest neighbors (KNN) algorithm.

django application - forecasting koare air pollution

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Forecasting air pollution from given weather data. Two models were implemented: Regression Trees and LSTM model.

IOT Based Air Pollution Monitoring & Forecasting System. A research based work for sensing air pollution along with location coordinates and designed website to store and monitor AQI data. Machine learning is used for hourly and weekly AQI forecast.

WebApp visualizing the pollution data received from CAMS European air quality forecasts.

This repository contains the code and resources for predicting PM2.5 levels using air quality data from OpenAQ. It includes data collection, preprocessing, and modeling scripts along with analysis and visualizations. The project aims to forecast PM2.5 levels and provide insights into air pollution trends.

Using numerical modeling to simulate fine dust. (Convection-Diffusion Equation)

WeatherWatch is a java application made for weather forecasting and air pollution insights.

This repository has been created for air pollution forecast in the coming hours in Beijing.

The Air Quality Prediction project is a machine learning initiative aimed at forecasting air pollution levels in a given area based on historical data and relevant environmental features.

A time-series forecasting model to predict air pollution levels for each day which enables maintenance of the air filtration system. This capstone project showcases how AWS IoT and Amazon SageMaker services can deliver an end-to-end IoT+ML project.