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

Yıl 2021, Sayı: 26 - Ejosat Özel Sayı 2021 (HORA), 364 - 368, 31.07.2021

Ege Alp Türkyener , Savaş Şahin , Sadık Arslan

https://doi.org/10.31590/ejosat.957500

Öz

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.

Proje Numarası

1139B412000704

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

Öz

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.

Anahtar Kelimeler

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

Destekleyen Kurum

Scientific and Technical Research Council of Turkey (TUBITAK)

Proje Numarası

1139B412000704

Kaynakça

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