Detection of anomalous Nitrogen Dioxide concentration of Ankara: a Reconstruction-based approach

Year 2024, EARLY VIEW, 1 - 1

Mustafa Murat Arat

https://doi.org/10.2339/politeknik.1419512

Abstract

Air quality significantly impacts human health, particularly in urban areas, leading to global morbidity and mortality. Elevated air pollutant levels pose health risks, emphasizing the need for timely monitoring and detection. This study adopts an innovative approach to identify anomalies of daily NO2 concentration levels in a district of Ankara, Turkey. Leveraging both traditional statistical approaches and state-of-the-art techniques, the research aims to provide real-time alerts. Employing a multivariate strategy, the study generates new features based on historical and current data, and incorporates periodic variables, as well. Among the methods explored, Variational Autoencoder emerges as noteworthy, exhibiting superior performance with %98 recall, %82 precision and %0.12 false alarm rate. This approach not only demonstrates a high true positive rate, enhancing its efficacy in anomaly detection but also effectively mitigates false alarms, preventing alert fatigue. By using advanced methodologies with a focus on NO2 levels, the study contributes to proactive measures for public health, enabling prompt responses to potential air quality issues.

Keywords

Air quality, anomaly detection, variational autoencoders, NO2 concentration, machine learning

Ankara'daki anormal Azot Dioksit konsantrasyonunun tespiti: Rekonstrüksiyona-dayalı bir yaklaşım

Abstract

Hava kalitesi, özellikle kentsel bölgelerde önemli ölçüde insan sağlığını etkilemekte ve tüm dünyada morbiditeye ve mortaliteye yol açmaktadır. Hava kirleticilerinin yüksek seviyeleri sağlık riski oluşturarak zamanında izleme ve tespitin gerekliliğini vurgulamaktadır. Bu çalışma, Ankara’nın bir ilçesindeki günlük NO2 konsantrasyon seviyesindeki anomalileri belirlemek için yenilikçi bir yaklaşım benimsemektedir. Geleneksel istatistiksel yöntemler ile birlikte son teknoloji teknikleri kullanan bu araştırma gerçek zamanlı uyarılar sağlamayı amaçlamaktadır. Çok değişkenli bir stratejiyi benimseyen çalışma, geçmiş ve güncel verilere dayalı yeni öznitelikleri ve ayrıca periyodik değişkenleri de analizlere dahil etmektedir. İncelenen yöntemler arasında, %98 duyarlılık, %82 kesinlik and %0.12 yanlış alarm oranı ile Varyasyonel Otokodlayıcı yöntemi üstün performans sergileyerek dikkat çekmektedir. Elde edilen model, sadece yüksek bir gerçek pozitif oranı elde etmekle kalmayıp, aynı zamanda yanlış alarmları etkili bir şekilde azaltarak alarm yorgunluğunu önlemektedir. NO2 seviyelerine odaklanan gelişmiş metodolojiler kullanan bu çalışma, halk sağlığına yönelik proaktif önlemlere katkıda bulunarak potansiyel hava kalitesi sorunlarına hızlı yanıt verilmesini sağlamaktadır.

Keywords

Hava kalitesi, anomali tespiti, varyasyonel otokodlayıcılar, NO2 konsantrasyonu, makine öğrenmesi

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