YZE
Projects Accepted in the Field of Climate Change Effects
Argate Engineering Project
Project Title: Artificial Intelligence Applications in Monitoring the Effects of Climate Change with New Generation Air Quality Measurement Systems Project
Customer Institution: Argate Engineering Software Information Industry and Trade Limited Company
Project Manager: Ibrahim Ural Bostanci
Consortium Members:
With the project; It is aimed to develop a new generation Air Quality Measurement System supported by artificial intelligence, able to communicate with each other, having effective data transfer features, and internet of things infrastructure, and to detect air pollution with high sensitivity, which is expected to increase with climate change. In addition, it is aimed to determine where measurement systems should be placed in the region in order to fully represent the air quality of the region of interest. In order to determine the optimum locations, within the scope of the project; It is aimed to develop an artificial intelligence-supported solution by considering factors such as the topography of the region, meteorological conditions and polluting sources.
In the project to be developed, an optimization model will be created to create an air quality monitoring network in which the next generation air quality systems will best represent the field. This new model will be designed to include environmental parameters such as meteorological conditions and topography, as well as economic constraints. Thanks to these artificial intelligence supported model outputs, it is aimed to provide measurements that best represent the air quality of the region to be measured, with minimum installation and operating costs.
It is aimed that the system to be developed will offer various advantages such as low cost equipment and low energy consumption, real-time operation, easy deployment in the field, and providing a large number of parameter measurements (on-site). Although low-cost sensors are advantageous due to their ease of use and economic aspects, their measurement reliability is not sufficient. It is expected that the development of a calibration solution with the support of artificial intelligence in order for the new generation air quality measurement system to be developed within the scope of this project to reach the measurement quality close to the conventional stations will make a great contribution to the reliability of the measurement quality of these systems.
