Large Scale Air-Quality Monitoring in Smart and Sustainable Cities

Smart Cities ◽  
2017 ◽  
pp. 725-753 ◽  
Author(s):  
Xiaofan Jiang
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Sustainable Cities ◽  
Air Quality Monitoring

scholarly journals Multiple Air Quality Monitoring Evidence of the Impacts of Large-scale Social Restrictions during the COVID-19 Pandemic in Jakarta, Indonesia

2021 ◽  
Vol 21 ◽  
Author(s):  
Muhayatun Santoso ◽  
Philip K. Hopke ◽  
Didin Agustian Permadi ◽  
Endah Damastuti ◽  
Diah Dwiana Lestiani ◽  
...  
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Predict and Measure Air Quality Monitoring System Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 2562-2571
Author(s):  
D.Saravanan , Et. al.
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Suitable Model ◽  
Air Quality Monitoring ◽  
Related Data ◽  
Multiple Tasks ◽  
Standard Regularization ◽  
Term Data

This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.

Indoor and outdoor air quality monitoring in hemp house — Case study

2011 ◽  
Vol 6 (3) ◽  
pp. 63-72 ◽  
Author(s):  
Jarmila Rimbalová ◽  
Silvia Vilčeková ◽  
Adriana Eštoková
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Quality ◽  
Indoor And Outdoor
Sign in / Sign up

Export Citation Format

Share Document