Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme

Yasemin GÜLTEPE

Makale detayları

Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme

Yazarlar:

Yasemin GÜLTEPE

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2019 Vol. Issue 16, pp.8-15

Belge Tipi:

Makale

Yayın Tarihi:

8/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000919887

Sıklık:

Üç Aylık

eISSN:

2148-2683

Anahtar kelimeler:

Özet:

 


Air pollution is one of the biggest problems of today. Air pollution, population growth, urban development and growth are increasingly important with the development of industry. Generally, the harmful effects of air pollutants on humans, animals and the environment show complex distribution patterns depending on time, space, duration of action, concentration and other characteristics. This complexity means that modeling and measurement of pollutant samples and trends is also difficult to predict the levels of pollution to which people are exposed. One of the most important steps in prevention of air pollution is the evaluation of contamination in a model. In this study, it is aimed to model air pollution by using some meteorological parameters in the estimation of air pollution by various machine learning algorithms which give new and successful results in meteorology and environment applications. Minimum-Max (Min-Max) normalization technique was used with learning methods. The performance values obtained in the study are compared with the similar studies in the literature and the most appropriate classification algorithm for the solution of the problem has been determined. Separate models were designed and analyzed by using methods such as Artificial Neural Networks (ANN), Random Forest, K-Nearest Neighborhood (K-NN), Logistic Regression, Decision Tree, Linear Regression and Naive Bayes. The performance values obtained in the study were compared with similar studies in the literature and the most appropriate estimation algorithm for the solution of the problem was determined. In this case, 70% of the data set is used for training and 30% for testing. As a result of the study, it was seen that the correct estimation rate for the ANN model was 87% and the other machine learning models gave the best results in the estimation with 99% of the Random Forest accuracy rate and 99% of the Decision Tree accuracy rate. The Linear Regression method performs poorly with a 30% accuracy rate. Performance evaluation of methods used on KastamonuDataSet in terms of the Explanatory Coefficient (R2), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. 

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Makale detayları

Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme

Yazarlar:

Yasemin GÜLTEPE

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2019 Vol. Issue 16, pp.8-15

Belge Tipi:

Makale

Yayın Tarihi:

8/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000919887

Sıklık:

Üç Aylık

eISSN:

2148-2683

Yapay Sinir Ağları

Hava Kirliliği

Kastamonu

Sayı

Makale detayları

Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme

Yazarlar: Yasemin GÜLTEPE

Yayıncı: Osman SAĞDIÇ

Ana Başlıklar: Uygulamalı fizik

Dergi: European Journal of Science and Technology 2019 Vol. Issue 16, pp.8-15

Belge Tipi: Makale

Yayın Tarihi: 8/2019

Belge Dili: Türkçe

Document ID (Org No.): 2000919887

Sıklık: Üç Aylık

eISSN: 2148-2683

Yapay Sinir Ağları

Hava Kirliliği

Kastamonu

Sayı

Yazarlar:

Yasemin GÜLTEPE

Yayıncı:

Osman SAĞDIÇ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

European Journal of Science and Technology 2019 Vol. Issue 16, pp.8-15

Belge Tipi:

Makale

Yayın Tarihi:

8/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000919887

Sıklık:

Üç Aylık

eISSN:

2148-2683