The Efficiency of Measure Particulate Matter Device using Classification Techniques and Association Rules Discovery for Factor Analysis Influencing Particulate Matter
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Abstract
The objectives of the research were to develop low-cost Measure Particulate Matter Device (PM2.5) with real-time display and analyze data using classification techniques and association rules discovery. The developed device has a microcontroller with a PM2.5 dust detector using infrared light, temperature and humidity sensor, and raindrop sensor. The device is being installed at the weather station in Laem Chabang Municipal Stadium (32T), Chonburi, Thailand. The weather station belongs to the Air Quality and Noise Management Bureau, Pollution Control Department, Ministry of Natural Resources and Environment. The data is collected every 20 seconds for 50 days, received a total of 196,023 records. The collected data is divided into 2 data sets; the first data set is the data that device and weather station measures at the same time and the second data set are the average hourly data of that device and weather station measure. The two sets of data compared to measure the efficiency of the device. The data was analyzed by data mining using Classification Techniques with Decision Tree algorithm and Association Rule with the Apriori algorithm. The results of this research showed that the device has the accuracy in measuring at ±0.5 (±5.77%) with hourly data set and has the accuracy in measuring at ±1.0 (±8.46%) with the hourly average data set. Decision Tree algorithm has accuracy for the forecast at 62.36%. Apriori algorithm gave the highest confidence in Association Rules at 78% with PM2.5 between 20.00 – 24.99 µg/m3 relation to a temperature between 30.00o – 34.99o C
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