Detection of Outliers and Extreme Events of Ground Level Particulate Matter Using DBSCAN Algorithm with Local Parameters

Aslan, Meryem Ezgi; Önüt, Semih

doi:10.1007/s11270-022-05679-6

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2024

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“…More specifically, the method was conducted on hourly and daily data, including nine pollutants and meteorological factors, and during data preprocessing, they identified outliers. Aslan et al (2022) use the DBSCAN algorithm to find outliers and characterize them as noise and extreme values, capitalizing on its independence from statistical assumptions and limited prior use.…”

Section: Introductionmentioning

confidence: 99%

Comparison of outlier detection approaches in a Smart Cities sensor data context

Zafeirelli,

Kavroudakis

2024

International Journal on Smart Sensing and Intelligent Systems

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This study examines outlier detection in time-series sensor data from PurpleAir low-cost sensors in Athens, Greece. Focusing on key environmental parameters such as temperature, humidity, and particulate matter (PM) levels, the study utilizes the Interquartile Range (IQR) and Generalized Extreme Studentized Deviate (GESD) methods on hourly and daily basis. GESD detected more outliers than IQR, most of them in PM, while temperature and humidity data had fewer outliers; applying filters before outlier detection and adjusting alpha values based on time scales were crucial, and outliers significantly affected spatial interpolation, emphasizing the need for spatial statistics in smart city air quality management.

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Section: Introductionmentioning

confidence: 99%