Assessment & Forecast of Air Quality data based on Neural Network

2020 ◽  
Vol 12 (01) ◽  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

Maximum likelihood cost functions for neural network models of air quality data

2003 ◽  
Vol 37 (24) ◽  
pp. 3435-3443 ◽  
Author(s):  
Stephen R Dorling ◽  
Robert J Foxall ◽  
Danilo P Mandic ◽  
Gavin C Cawley
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Maximum Likelihood ◽  
Network Models ◽  
Quality Data ◽  
Cost Functions ◽  
Neural Network Models ◽  
Air Quality Data

scholarly journals Making Air Quality Data Meaningful

2020 ◽  
Author(s):  
Szu-Yu (Cyn) Liu ◽  
Justin Cranshaw ◽  
Asta Roseway
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

Feature Selection and Analysis in Air Quality Data

2021 ◽  
Author(s):  
Manish Mahajan ◽  
Santosh Kumar ◽  
Bhasker Pant ◽  
Umesh Kumar Tiwari ◽  
Rijwan Khan
Keyword(s):  
Feature Selection ◽  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

2021 ◽  
Vol 138 ◽  
pp. 104976
Author(s):  
Juan José Díaz ◽  
Ivan Mura ◽  
Juan Felipe Franco ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Web Based ◽  
Air Quality Data
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