自强不吸——基于上海市天气与交通状况的空气质量分析报告
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Updated
Mar 20, 2018 - Python
自强不吸——基于上海市天气与交通状况的空气质量分析报告
Build an iOS Application to Predict Air Pollution Using a Random Forest Regressor
Data Assimilation with ML/DL methods
Command-line application providing some information about air quality in Poland. Tested using Mockito and JUnit
Used Tensorflow Object Detection API to detect different vehicles in a picture and predict pollution levels.
Using long short term memory networks to analysis the pollution of Beijing, China.
Time series prediction models, exploratory data analysis and clustering on air pollution data
Research Paper on the prediction of pollutants concentration in Lille using Machine Learning Methods
If you liked my analysis, pls upvote my notebook!
Creation of an ecological carpooling app with the possibility of doing stopovers
A Python notebook that aims to analyze the change in air quality index (AQI) for 4 major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide, Ozone) that cover all 50 states of the United States from 2000 to 2016.
This is the repository that contains CAbc-QUAL, model for hydrological and load modelling.
Prediction of PM 2.5 using transfer learning approaches.
Zephyr is a platform which provides users with the predicted Air Quality Index levels of air pollution for 39 cities of India with daily, monthly and yearly trends. It also provides some of the statistics observed for AQI over these cities and various latest articles and blogs related to air pollution.
Zephyr is a platform which provides users with the predicted AQI levels of air pollution for 39 cities of India with daily, monthly and yearly trends. It also provides some of the statistics observed for AQI over these cities and various latest articles and blogs related to air pollution.
Python tool to sense, predict and push pollution data to Firebase Realtime Database
Ongoing research on several projects..
SCMPM: An open source and efficient calibration and mapping approach based on real-time spatial model that calibrates measurements from low-cost sensor in an environment with high relative humidity. The model provides spatial calibration of low cost PM2.5 sensors
This project aims to develop machine learning models to forecast PM2.5 levels in the Jeongnim-Dong area from 2018 to 2022.
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