Ege Alp TÜRKYENER, Savaş ŞAHİN, Sadık ARSLAN

IoT Tabanlı Hava Kalitesi Ölçer Modülünün Tasarımı ve Makine Öğrenmesi ile Hava Kalitesi Analizi

Bu çalışma, Nesnelerin İnterneti kullanılarak şehirlerdeki PM2.5 ve PM10 parçacıklarının etkisini gerçek zamanlı olarak analiz etmek için toplu taşıma araçlarına yerleştirilen ARM tabanlı bir hava kalitesi modülünü önermektedir. STM32 mikrodenetleyicisi, PM'den ve nem, sıcaklık sensörlerinden veri elde etmek için kullanılır. Sensörlerden toplanan veriler, ethernet modülü ile internet portalına bağlanan i.MX6UL’ya, RS-485 kullanılarak iletilir. i.MX6UL, verileri çevrimiçi olarak Microsoft Azure Hub'a gönderir ve bilgisayar aracılığıyla da kaydedilir. Elde edilen veriler hava kalitesi-meteorolojik değişkenler için analiz edilmiş ve regresyon modelleri makine öğrenme algoritmaları ile uygulanmıştır. PM2.5, PM10, nem ve sıcaklık verileri R2 testi ve regresyon modelleri için ortalama karekök hatası ile değerlendirilmiştir. Rastgele orman algoritması, kullanılan diğer regresyon modelleri arasında en iyi sonucu göstermiştir.

Anahtar Kelimeler:

hava kalitesi analizi, gerçek zamanlı izleme, makine öğrenimi, nesnelerin internet, arm tabanlı mikroişlemci.

Design of IoT-based Air Quality Meter Module and Air Quality Analysis with Machine Learning

This study proposes an ARM based air quality module placed to public transport vehicles for analyzing the effect of PM2.5 and PM10 particles in the cities in real-time using Internet of Things. The STM32 microcontroller is used for obtaining the data from the PM, humidity, and temperature sensors. The data collected from the sensors are sent to the i.MX6UL microprocessor using RS-485 connected to the internet portal with an Ethernet module. The microprocessor sends the data to the Microsoft Azure Hub in-on-line, and it is also recorded via the computer. The obtained data is analyzed for air quality-meteorological variables and the regression models are implemented via machine learning algorithms. PM2.5, PM10, humidity and temperature data are evaluated with R2 test and root mean square error for regression models. The Random Forest algorithm shows better results among other used regression models.

Keywords:

air quality analysis, real-time monitoring, machine learning, internet of things, arm-based microprocessor,

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