Citation

Kok, Adrian Eng Hock and Chan, Yee Kit and Koo, Voon Chet (2024) Development of Real Time Meteorological Grade Monitoring Stations with AI Analytics. International Journal of Advanced Computer Science and Applications, 15 (9). ISSN 2158-107X

Text Development of Real Time Meteorological Grade Monitoring Stations with AI Analytics.pdf - Published Version Restricted to Repository staff only Download (796kB)

Abstract

Air pollution comes in many forms and the basis of measure is the concentration of particles in the air. The quality of air depends on the quantity of pollution measured by a particle sensor that is accurate down to micron-meter consistencies. The size of the pollutants will be ingested by humans and cause respiratory problems and its effects on health conditions. The research will study the measurement of particles using multiple types of light scattering sensors and reference them to the accuracy of meteorological standards for precision in measurement. The sensors will be subjected to extreme conditions to gauge the repeatability and behavior and also long-term deployment usage. This study is required as when deployed on the field, dust particles will degrade the sensors over time. Early detection of sensor sensitivity and maintenance is therefore considered part of the research. Air particle data is volatile and dynamic over time and with that said, mass deployment of these sensors will give a better measurement of pollution data. However, with more and more data, standard statistics used show a basic level indicator and hence the idea of using machine learning algorithms as part of artificial intelligence (AI) processing is adapted for analyzing and also predicting particle data. There is a foreseeable challenge on this as there is no one machine learning for use only for this and multiple models are considered and gauged with the best accuracy using R2 value as low as 0.75 during the entire research. Lastly, with the seamless Internet of Things sensing architecture, the improved spatial data resolution will be improved and can be used to complement the current pollution measurement data for Malaysia in particular.

Item Type:	Article
Uncontrolled Keywords:	Neural networks, AI, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Dec 2024 03:00
Last Modified:	05 Dec 2024 03:00
URII:	http://shdl.mmu.edu.my/id/eprint/13248

Downloads

Edit (login required)