Evaluation and Prediction of Air Quality in Kayseri Organized Industrial Zone By Using ANNs

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Uluslararası Çevresel Eğilimler Dergisi
Cilt: 9 Sayı: 1
Evaluation and Prediction of Air Quality in Kayseri Organized Industrial Zone By Using ANNs

Evaluation and Prediction of Air Quality in Kayseri Organized Industrial Zone By Using ANNs

Authors : Serkan Şahinkaya

Pages : 33-47

View : 28 | Download : 59

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :Industrial activities cause air pollution such as motor vehicle traffic, construction activities, energy production, as well as waste management. Air pollution has diverse adverse effects on human health and environmental health. Therefore, the environmental monitoring of air quality is very important for public health and environmental health protection. Such monitoring is assisted easily by artificial intelligence (AI). AI technologies such as artificial neural networks (ANNs) have started to receive wider attention in recent times for monitoring and modeling air pollution. Because these technologies facilitate easier and more accurate data processing and analysis which, therefore, aids in the estimation of air pollution levels. In this research, data relating to PM2.5, PM10, and SO2 levels collected from January 1, 2020 to November 1, 2024 at the air quality monitoring station in Kayseri Organized Industrial Zone are analyzed. The study is conducted in two stage. The first part deals with factors affecting the observations in this long period. The second part involves using a multilayer perceptron (MLP) artificial neural network model to predict the PM2.5, PM10, and SO2 levels. The data covering the period from January 1, 2020 to January 1, 2024 were applied to train the artificial intelligence model for modeling purposes, while those from January 1, 2024 to November 1, 2024 were employed for the validation of the model. In this step, ANNs can identify and exclude missing or unusual measurements. It was determined that the MLP model can be used for air pollution modelling. In addition, the consistency of the model was discussed and climatic data can be included to improve it.
Keywords : Air Quality, ANNs, PM2.5, PM10, SO2.

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