Yapay Zekâ Tabanlı Hava Kalitesi İyileştirme Stratejilerinin Değerlendirilmesi

Yıl 2024, , 87 - 97, 30.07.2024

Tuğçe Pekdoğan

https://doi.org/10.61512/emobd.1498119

Öz

Günümüzde hava kirliliği, kentsel ve sanayi bölgelerinde yaşayan milyonlarca insan için ciddi sağlık riskleri oluşturmaktadır. Bu makalede, yapay zekâ (AI) teknolojileri ve makine öğrenimi algoritmalarının hava kalitesini izleme ve iyileştirme stratejilerinin geliştirilmesinde nasıl kullanılabileceği ele alınmıştır. Bu araştırma, özellikle kentsel alanlarda hava kalitesi üzerinde etkili olan ana kirleticilerin dinamiklerini modellemek için makine öğrenmesi yaklaşımlarını kullanmaktadır.
Bu çalışmada, çeşitli yapay zekâ modelleri (RF, SVM, ANN, CNN, RNN, GAN) kullanılarak hava kalitesi verilerinin analiz, tahmin ve simüle edilmesi süreçleri detaylı bir şekilde incelenmiştir. Ayrıca, bu modellerin hava kalitesi yönetimi için stratejik karar verme süreçlerinde nasıl entegre edilebileceği üzerinde durulmuştur. Yapay zekâ tabanlı modeller, gerçek zamanlı veri akışını analiz ederek, hava kalitesi üzerinde olumlu etkiler yaratabilecek müdahaleler önermektedir.

Anahtar Kelimeler

Hava Kalitesi, Derin Öğrenme, Yapay Zekâ, Makine Öğrenmesi, Yapay Sinir Ağları

Etik Beyan

Bu çalışmada herhangi bir etik ihlal söz konusu olmamıştır.

Evaluation of Artificial Intelligence-Based Air Quality Improvement Strategies

Öz

Today, air pollution causes serious health risks for millions living in urban and industrialized areas. This paper discusses how artificial intelligence (AI) technologies and machine learning algorithms can be used to develop air quality monitoring and improvement strategies. This research uses machine learning approaches to model the dynamics of the main pollutants that influence air quality, especially in urban areas.
In this study, the processes of analyzing, predicting and simulating air quality data using various artificial intelligence models (RF, SVM, ANN, CNN, RNN, GAN) are examined in detail. Furthermore, how these models can be integrated into strategic decision-making processes for air quality management is emphasized. By analyzing the real-time data flow, AI-based models suggest interventions that can positively impact air quality.

Anahtar Kelimeler

Air Quality, Deep Learning, Artificial Intelligence, Machine Learning, Artificial Neural Networks

Kaynakça

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