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

Year 2016, , 605 - 616, 02.09.2016

Öznur İşçi Güneri , Nevin Güler Dinçer , Muhammet Oğuzhan Yalçın

https://doi.org/10.16984/saufenbilder.69439

Abstract

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.

Keywords

Otoregresif model, bulanık k-medoid kümeleme, hava kalitesi izleme istasyonları

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

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.  

Keywords

Autoregressive model, fuzzy k-medoids clustering, air quality monitoring stations

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