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Journals / Sakarya University Journal of Science / 2016 / Cilt: 20 Sayı: 3

Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey

Zaman Serisi Kümelemesinin Türkiye’deki hava kirliliği izleme istasyonlarındaki bilgi fazlalığının tespit edilmesine uygulanması

Öznur İŞÇİ GÜNERİ
Nevin GÜLER DİNÇER
Muhammet Oğuzhan YALÇIN
Pages
605–616
Publication Date
2016-09-02
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

At the results of analyses, it has been concluded that information redundancy in monitoring stations and thus monitoring cost can be decreased approximately 78.5% for PM10 air pollutant, 73.5% for SO2 air pollutant.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of study is to determine the monitoring stations having similar behavior with respect to PM10 and SO2 concentrations and thus decrease monitoring cost and information redundancy.
📊
Key Findings & Data:For this purpose, autoregressive model based Fuzzy K-medoids algorithm is used.
💡
Conclusion & Impact:At the results of analyses, it has been concluded that information redundancy in monitoring stations and thus monitoring cost can be decreased approximately 78.5% for PM10 air pollutant, 73.5% for SO2 air pollutant.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Otoregresif model · bulanık k-medoid kümeleme · hava kalitesi izleme istasyonları

Abstract

The aim of study is to determine the monitoring stations having similar behavior with respect to PM10 and SO2 concentrations and thus decrease monitoring cost and information redundancy. For this purpose, autoregressive model based Fuzzy K-medoids algorithm is used. At the results of analyses, it has been concluded that information redundancy in monitoring stations and thus monitoring cost can be decreased approximately 78.5% for PM10 air pollutant, 73.5% for SO2 air pollutant.

Özet

Bu çalışmanın amacı Türkiye’de PM10 ve SO2 kirleticileri konsantrasyonları bakımından benzer davranışa sahip hava kirliliği izleme istasyonlarını belirlemek ve böylece izleme maliyetini ve bilgi fazlalığını azaltmaktır. Bu amaca yönelik olarak, otoregresif modele dayanan Bulanık k-Medoidler (BKM) algoritması kullanılmıştır. Yapılan analizler sonucunda, izleme istasyonlarındaki bilgi fazlalılığının ve bununla birlikle izleme maliyetinin PM10 hava kirleticisi için yaklaşık olarak %78.5, SO2 hava kirleticisi için %73.5 azaltılabileceği sonucunda ulaşılmıştır.

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Cite This Article

GÜNERİ, Ö. İ., DİNÇER, N. G., YALÇIN, M. O. (2016). Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. Sakarya University Journal of Science, Cilt: 20 Sayı: 3, 605–616. https://ajindex.com/articles/942380

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