Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Modeling air pollution by integrating ANFIS and metaheuristic algorithms
Modeling Earth Systems and Environment · Haziran 2023
YÖKSİS Kayıtları
Modeling air pollution by integrating ANFIS and metaheuristic
algorithms
Modeling Earth Systems and Environment · 2023 ESCI
DOKTOR ÖĞRETİM ÜYESİ AYNUR YONAR →
Modeling air pollution by integrating ANFIS and metaheuristic algorithms
MODELING EARTH SYSTEMS AND ENVIRONMENT · 2022 Emerging Sources Citation Index
DOKTOR ÖĞRETİM ÜYESİ HARUN YONAR →
Makale Bilgileri
DergiModeling Earth Systems and Environment
Yayın TarihiHaziran 2023
Cilt / Sayfa9 · 1621-1631
Scopus ID2-s2.0-85140829979
Erişim🔓 Açık Erişim
Özet
Air pollution is increasing for many reasons, such as the crowding of cities, the failure of planning to consider the benefit of society and nature, and the non-implementation of environmental legislation. In the recent era, the impacts of air pollution on human health and the ecosystem have become a primary global concern. Thus, the prediction of air pollution is a crucial issue. ANFIS is an artificial intelligence technique consisting of artificial neural networks and fuzzy inference systems, and it is widely used in estimating studies. To obtain effective results with ANFIS, the training process, which includes optimizing its premise and consequent parameters, is very important. In this study, ANFIS training has been performed using three popular metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) for modeling air pollution. Various air pollution parameters which are particular matters: PM2.5 and PM10, sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and several meteorological parameters such as wind speed, wind gust, temperature, pressure, and humidity were utilized. Daily air pollution predictions in Istanbul were obtained using these particular matters and parameters via trained ANFIS approaches with metaheuristics. The prediction results from GA, PSO, and DE-trained ANFIS were compared with classical ANFIS results. In conclusion, it can be said that the trained ANFIS approaches are more successful than classical ANFIS for modeling and predicting air pollution.
Yazarlar (2)
1
Aynur Yonar
2
Harun Yonar
Anahtar Kelimeler
Air pollution
ANFIS
Artificial intelligence
Metaheuristics
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
41
Atıf
2
Yazar
4
Anahtar Kelime