Time series analysis and forecasting of air quality index

2021 ◽  
Author(s):  
M. C. Lineesh
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Time Series Analysis ◽  
Quality Index ◽  
Air Quality Index ◽  
Series Analysis

scholarly journals Health benefit of air quality improvement in Guangzhou, China: Results from a long time-series analysis (2006–2016)

2019 ◽  
Vol 126 ◽  
pp. 552-559 ◽  
Author(s):  
Rongshan Wu ◽  
Xiaoming Song ◽  
Duohong Chen ◽  
Liuju Zhong ◽  
Xiaoliang Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Improvement ◽  
Air Quality ◽  
Time Series Analysis ◽  
Health Benefit ◽  
Series Analysis ◽  
Long Time Series ◽  
Long Time ◽  
Air Quality Improvement

Time-series analysis of Riverside, California air quality data

1975 ◽  
Vol 9 (11) ◽  
pp. 978-989 ◽  
Author(s):  
D.P. Chock ◽  
T.R. Terrell ◽  
S.B. Levitt
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Time Series Analysis ◽  
Quality Data ◽  
Series Analysis ◽  
Air Quality Data

scholarly journals Prediction and Forecasting of Air Quality Index in Chennai using Regression and ARIMA time series models

2021 ◽  
Vol 9 ◽  
Author(s):  
Geetha Mani ◽  
◽  
Joshi Kumar Viswanadhapalli ◽  
Albert Alexander Stonie ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Air Quality ◽  
Linear Regression ◽  
Quality Index ◽  
Air Quality Index ◽  
Model Parameters ◽  
Sensor Output ◽  
Model Accuracy ◽  
Life On Earth

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of non-renewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily,Multi Linear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O3), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB) – Chennai region, India feed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices(KPI) like co-efficient of determination, root mean square error and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%.

Forecasting of Highway Ecological Environment Evolution Based on Time Series Analysis

2013 ◽  
Vol 361-363 ◽  
pp. 1604-1610
Author(s):  
Guo Xiong Wu ◽  
Qing Jie Li ◽  
Gao Yun Cheng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Environmental Protection ◽  
Quality Index ◽  
Arma Model ◽  
Ecological Environment ◽  
The Road ◽  
Environment Quality ◽  
Series Analysis ◽  
Environment Evolution

A roads ecological environmental quality index data for years is a time-series, and accurate prediction by exploring the changing rule of the time series is significant for future environmental protection. For this end, based on time series analysis, this paper firstly carries out steady analysis and random test pretreatment for the eco-environment quality index (EQI) data collected. Next, an ARMA model is established by pattern recognition, established bands and parameter estimation, and then verified by the data collected in recent years. Finally, the ARMA model is used to predict the future EQI values. This research will provide effective guidance to the eco-environmental protection along the road.

scholarly journals Time Series Analysis of Venice Air Quality Data

1977 ◽  
Vol 10 (7) ◽  
pp. 295-300
Author(s):  
Paolo Zannetti ◽  
Giovanna Finzi ◽  
Giorgio Fronza ◽  
Sergio Rinaldi
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Time Series Analysis ◽  
Quality Data ◽  
Series Analysis ◽  
Air Quality Data

scholarly journals Time Series Analysis of Air Quality in Chennai city

2021 ◽  
Vol 1770 (1) ◽  
pp. 012106
Author(s):  
M. Nirmala ◽  
M. Mallika ◽  
M. Gomathi
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Chennai City
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