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Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme

Yıl 2025, Cilt: 11 Sayı: 2, 471 - 487, 27.07.2025
https://doi.org/10.21324/dacd.1628030

Öz

Kentleşme, enerji tüketimi, sanayileşme ve nüfus artışıyla birlikte hava kirliliği ve hava kalitesindeki düşüş, halk sağlığı ve çevre üzerinde ciddi bir tehdit oluşturmaktadır. Kirleticilerin tespiti ve kontrolü, günümüzün öncelikli sorunlarından biri haline gelmiş ve bu bağlamda yapay zekâ tabanlı yöntemlerin hava kirliliği araştırmalarında artan önemi dikkat çekmiştir. Bu çalışma, 2004–2024 yılları arasında yapay zekâ tabanlı yöntemlerin kullanıldığı hava kirliliği araştırmalarındaki öncelikli temaların gelişimini ve gelecekteki araştırmalara yön verebilecek alanları kapsamlı bir şekilde incelemiştir. Bibliyometrik analiz ve atıf analizi yöntemleri kullanılarak yapılan bu araştırma, literatürün sistematik bir değerlendirmesini sunmuş ve hava kirliliği ile ilgili yapay zekâ uygulamalarının zamanla üstel bir artış gösterdiğini ortaya koymuştur. Araştırma sonuçları, 2014 sonrası dönemde yapay zekâ tabanlı yöntemlerin literatürde bir paradigma değişimi yaratarak hava kirliliği tahminleri ve modellemelerinde merkezi bir rol üstlendiğini göstermektedir. Aynı zamanda, disiplinler arası iş birliği eğilimlerinin güçlenmekte olduğunu ve yapay zekâ tabanlı yöntemlerin yalnızca yenilikçi bir çözüm sunmakla kalmayıp, aynı zamanda literatürdeki evrimi şekillendiren bir dönüşüm sağladığını ortaya koymaktadır. Bu analiz, alanın mevcut durumunu anlamak ve gelecekteki araştırma yönelimlerini belirlemek için değerli bir bilgi kaynağı sunmakta, yapay zekâ tabanlı yöntemlerin hava kirliliği çalışmalarında daha geniş ve etkili bir şekilde kullanılabileceğini güçlü bir şekilde desteklemektedir.

Etik Beyan

Yok

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

Verilerin analiz edilmesi ve anlamlı sonuçların elde edilmesinde güçlü istatistiksel ve grafiksel yetenekleriyle katkı sağlayan Bibliometrix R paketi (http://www.bibliometrix.org) içerisindeki bibliShiny yazılımı geliştiricilerine, ayrıca araştırmanın temelini oluşturan kapsamlı bibliyometrik verileri sağlayan Clarivate Analytics Web of Science, WoS veritabanına teşekkür edilmektedir.

Kaynakça

  • Ansari, A., & Quaff, A. R. (2024). Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. Environmental Research and Technology, 7(3), 356–377. https://doi.org/10.35208/ert.1434390
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
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  • Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., Guo, J., Ren, H., Abramson, M. J., & Guo, Y. (2018). A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Science of the Total Environment, 636, 52–60. https://doi.org/10.1016/j.scitotenv.2018.04.251
  • Chen, J., Chen, Q., Hu, L., Yang, T., Yi, C., & Zhou, Y. (2024). Unveiling trends and hotspots in air pollution control: a bibliometric analysis. Atmosphere, 15, Article 630. https://doi.org/10.3390/atmos15060630
  • da Silva Filho, A. M., da Silva, I. J., Queila, K., Brito, D., Sobrinho, T. G., & Chaves, L. H. G. (2020). Air pollution: bibliometric analysis and space-temporal distribution of specialized scientific production. International Journal of Advanced Engineering Research and Science, 7(10), 331–341. https://dx.doi.org/10.22161/ijaers.710.38
  • Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Article 104909. https://doi.org/10.1016/j.envint.2019.104909
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285296. https://doi.org/10.1016/j.jbusres.2021.04.070
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  • Fan, H., Zhao, C., & Yang, Y. (2020). A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment, 220, Article 117066. https://doi.org/10.1016/j.atmosenv.2019.117066
  • Feng, T., Sun, Y., Shi, Y., Ma, J., Feng, C., & Chen, Z. (2024). Air pollution control policies and impacts: a review. Renewable and Sustainable Energy Reviews, 191, Article 114071. https://doi.org/10.1016/j.rser.2023.114071
  • Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., & Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: a bibliometric analysis. Frontiers in Public Health, 10, Article 933665. https://doi.org/10.3389/fpubh.2022.933665
  • Hamer, P. D., Walker, S-E., Sausa Santos, G., Vogt, M., Vo Thanh, D., Lopez Aparicio, S., Schneider, P., Ramacher, M. O. P., & Karl, M. (2020). The urban dispersion model EPISODE v10.0– Part 1: an eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions. Geoscientific Model Development, 13, 4323–4353. https://doi.org/10.5194/gmd-13-4323-2020
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  • Houdou, A., Badisy, I. E., Khomsi, K., Abdala, S. A., Abdulla, F., Najmi, H., Obtel, M., Belyamani, L., Ibrahimi, A., & Khalis, M. (2024). Interpretable machine learning approaches for forecasting and predicting air pollution: a systematic review. Aerosol and Air Quality Research, 24(1), Article 230151. https://doi.org/10.4209/aaqr.230151
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  • Li, T., Shen, H., Yuan, Q., Zhang, X. X., & Zhang, L. (2017c). Estimating ground-level PM2.5 by fusing satellite and station observations: a geo-intelligent deep learning approach. Geophysical Research Letters, 44(23), 985–993. https://doi.org/10.1002/2017GL075710
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Development and Future Perspectives of Air Pollution Research Using Artificial Intelligence-Based Methods: A Bibliometric Review

Yıl 2025, Cilt: 11 Sayı: 2, 471 - 487, 27.07.2025
https://doi.org/10.21324/dacd.1628030

Öz

Urbanization, energy consumption, industrialization, and population growth, along with air pollution and the decline in air quality, pose a serious threat to public health and the environment. The detection and management of pollutants have become urgent global concerns, underscoring the growing significance of artificial intelligence (AI)-based methods in air pollution research. This study presents a thorough review of the evolution of key themes in AI-driven air pollution research from 2004 to 2024, highlighting areas for future investigation. Through bibliometric and citation analyses, the study systematically examines the literature, revealing an exponential growth in AI applications in air pollution research over time. The findings indicate that after 2014, AI-based methods have led to a paradigm shift, playing a critical role in air pollution forecasting and modeling. At the same time, the study reveals that interdisciplinary collaboration trends are strengthening and that AI-based approaches not only offer innovative solutions but also serve as a transformative force shaping the evolution of the literature. This analysis provides valuable insights into the current state of air pollution research and presents guidance for future directions, emphasizing the need for broader and more effective integration of AI techniques in this area.

Proje Numarası

Yok

Kaynakça

  • Ansari, A., & Quaff, A. R. (2024). Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database. Environmental Research and Technology, 7(3), 356–377. https://doi.org/10.35208/ert.1434390
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Awasthi, A., Pattnayak, K. C., Tiwari, P. R., Panda, S. K., Gautam, S., Choudhury, T., & Sar, A. (2024). Air quality monitoring (AQM) and prediction. In A. Awasthi, K. C. Pattnayak, G. Dhiman, & P. R. Tiwari (Eds.), Artificial intelligence for air quality monitoring and prediction (Vol. 1, pp. 1–23). CRC Press.
  • Birkle, C., Pendlebury, D., Schnell, J., & Adams, J. (2020). Web of science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1, Article 3550, 1–14. https://doi.org/10.1162/qss_a_00018
  • Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., Guo, J., Ren, H., Abramson, M. J., & Guo, Y. (2018). A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Science of the Total Environment, 636, 52–60. https://doi.org/10.1016/j.scitotenv.2018.04.251
  • Chen, J., Chen, Q., Hu, L., Yang, T., Yi, C., & Zhou, Y. (2024). Unveiling trends and hotspots in air pollution control: a bibliometric analysis. Atmosphere, 15, Article 630. https://doi.org/10.3390/atmos15060630
  • da Silva Filho, A. M., da Silva, I. J., Queila, K., Brito, D., Sobrinho, T. G., & Chaves, L. H. G. (2020). Air pollution: bibliometric analysis and space-temporal distribution of specialized scientific production. International Journal of Advanced Engineering Research and Science, 7(10), 331–341. https://dx.doi.org/10.22161/ijaers.710.38
  • Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Article 104909. https://doi.org/10.1016/j.envint.2019.104909
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • European Union. (2021). Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal of the European Union. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:152:0001:0044:en:PDF
  • Fan, H., Zhao, C., & Yang, Y. (2020). A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment, 220, Article 117066. https://doi.org/10.1016/j.atmosenv.2019.117066
  • Feng, T., Sun, Y., Shi, Y., Ma, J., Feng, C., & Chen, Z. (2024). Air pollution control policies and impacts: a review. Renewable and Sustainable Energy Reviews, 191, Article 114071. https://doi.org/10.1016/j.rser.2023.114071
  • Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., & Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: a bibliometric analysis. Frontiers in Public Health, 10, Article 933665. https://doi.org/10.3389/fpubh.2022.933665
  • Hamer, P. D., Walker, S-E., Sausa Santos, G., Vogt, M., Vo Thanh, D., Lopez Aparicio, S., Schneider, P., Ramacher, M. O. P., & Karl, M. (2020). The urban dispersion model EPISODE v10.0– Part 1: an eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions. Geoscientific Model Development, 13, 4323–4353. https://doi.org/10.5194/gmd-13-4323-2020
  • Hau, Y. S. (2024). Artificial intelligence and air pollution: a bibliometric analysis from 2012 to 2022. International Journal of Advanced Smart Convergence, 13(1), 48–56. https://dx.doi.org/10.7236/IJASC.2024.13.1.48
  • Houdou, A., Badisy, I. E., Khomsi, K., Abdala, S. A., Abdulla, F., Najmi, H., Obtel, M., Belyamani, L., Ibrahimi, A., & Khalis, M. (2024). Interpretable machine learning approaches for forecasting and predicting air pollution: a systematic review. Aerosol and Air Quality Research, 24(1), Article 230151. https://doi.org/10.4209/aaqr.230151
  • Huang, C., & Kuo, P. (2018). A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors, 18, Article 2220. https://doi.org/10.3390/s18072220
  • Joharestani, M. Z., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere, 10, Article 373. http://doi.org/10.3390/atmos10070373
  • Koseoglu, M. A., Rahimi, R., Okumus, F., & Liu, J. (2016). Bibliometric studies in tourism. Annals of Tourism Research, 61, 180–198. https://doi.org/10.1016/j.annals.2016.10.006
  • Li, Y., Wang, Y., Rui, X., Li, Y., Li, Y., Wang, H., Zuo, J., & Tong, Y. (2017a). Sources of atmospheric pollution: a bibliometric analysis. Scientometrics, 112, 1015–1045. https://doi.org/10.1007/s11192-017-2421-z
  • Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017b). Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environmental Pollution, 231, 997–1004. https://doi.org/10.1016/j.envpol.2017.08.114
  • Li, T., Shen, H., Yuan, Q., Zhang, X. X., & Zhang, L. (2017c). Estimating ground-level PM2.5 by fusing satellite and station observations: a geo-intelligent deep learning approach. Geophysical Research Letters, 44(23), 985–993. https://doi.org/10.1002/2017GL075710
  • Li, K., Rollins, J., & Yan, E. (2018). Web of science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115, 1–20. https://doi.org/10.1007/s11192-017-2622-5
  • Li, Y., & Sun, Y. (2021). Modeling and predicting city-level CO2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 28(15), 19260–19271. https://doi.org/10.1007/s11356-020-12294-7
  • Li, Y., Sha, Z., Tang, A., Goulding, K., & Liu, X. (2023). The application of machine learning to air pollution research: a bibliometric analysis. Ecotoxicology and Environmental Safety, 257, Article 114911. https://doi.org/10.1016/j.ecoenv.2023.114911
  • Lin, H. (2024). Bibliometric analysis of traffic-related air pollution: using citeSpace to explore the knowledge structure and trends. Environmental Research Communications, 6, Article 022002. https://doi.org/10.1088/2515-7620/ad2a92
  • Luo, X., Yang, Q., Zheng, D., Tian, H., Chen, L., Wu, J., Ji, Z., Chen, Y., & Li, Z. (2022). A bibliometric and visualization analysis on the association between chronic exposure to fine particulate matter and cancer risk. Frontiers in Public Health, 10, Article 1039078. https://doi.org/10.3389/fpubh.2022.1039078
  • Mandal, S., Boppani, S., Dasari, V., & Thakur, M. (2024). A bivariate simultaneous pollutant forecasting approach by unified spectro-spatial graph neural network (USSGNN) and its application in prediction of O3 and NO2 for New Delhi, India. Sustainable Cities and Society, 114, Article 105741. https://doi.org/10.1016/j.scs.2024.105741
  • Marrakchi, N., Bergam, A., Fakhouri, H., & Khomsi, K. (2024). Air quality predicting using lstm recurrent neural network in Tangier: a comparative analysis detecting ozone concentration peaks. Palestine Journal of Mathematics, 13(1), 209–221.
  • Marvi, R., & Foroudi, M. M. (2023). Bibliometric analysis. In P. Foroudi & C. Dennis (Eds.), Researching and analysing business: research methods in practice (Vol. 3, pp. 4354), Routledge. https://doi.org/10.4324/9781003107774-4
  • Masood, A., & Ahmad, K. (2021). A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: fundamentals, application and performance. Journal of Cleaner Production, 322, Article 129072. https://doi.org/10.1016/j.jclepro.2021.129072
  • Mehmood, K., Bao, Y., Saifullah, C. W., Khan, M. A., Siddique, N., Abrar, M. M., Soban, A., Fahad, S., & Naidu, R. (2022). Predicting the quality of air with machine learning approaches: current research priorities and future perspectives. Journal of Cleaner Production, 379, Article 134656. https://doi.org/10.1016/j.jclepro.2022.134656
  • Menares, C., Perez, P., Parraguez, S., & Fleming, Z. L. (2021). Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks. Urban Climate, 38, Article 100906. https://doi.org/10.1016/j.uclim.2021.100906
  • Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, Md M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., & Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: how far have they gone?. Environment International, 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018
  • Nazli, S. N., Vilcins. D., Sly, P. D., Razak, A. A., Sabri, N., & Ibrahim, T. N. B. T. (2024). Indoor air quality: bibliometric analysis of the published literature between 2018 and 2023. Environmental Quality Management, 34, Article e22297. https://doi.org/10.1002/tqem.22297
  • Polezer, G., Tadano, Y. S., Siqueira, H. V., Godoi, A. F. L., Yamamoto, C. I., de André, P. A., Pauliquevis, T., Andrade, M. F., Oliveira, A., Saldiva, P. H. N., Taylor, P. E., & Godoi, R. H. M. (2018). Assessing the impact of PM2.5 on respiratory disease using artificial neural networks. Environmental Pollution, 235, 394–403. https://doi.org/10.1016/j.envpol.2017.12.111
  • Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019). A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1–10. https://doi.org/10.1016/j.scitotenv.2019.01.333
  • Rakholia, R., Le, Q., Vu, K., Ho, B. Q., & Carbajo, R. S. (2022). AI-based air quality PM2.5 forecasting models for developing countries: a case study of Ho Chi Minh City, Vietnam. Urban Climate, 46, Article 101315. https://doi.org/10.1016/j.uclim.2022.101315
  • Ramadan, M. N. A., Ali, M. A. H., Khoo, S. Y., Alkhedher, M., & Alherbawi, M. (2024). Real-time IoT-powered AI system for monitoring and forecasting of air. pollution in industrial environment. Ecotoxicology and Environmental Safety, 283, Article 116856. https://doi.org/10.1016/j.ecoenv.2024.116856
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  • Schneider, A., Friedl, M. A., & Potere, D. (2010). Mapping global urban areas using MODIS 500-m data: new methods and datasets based on ‘urban ecoregions’. Remote Sensing of Environment, 114(8), 1733–1746. https://doi.org/10.1016/j.rse.2010.03.003
  • Song, X. H., & Hopke, P. K. (1996). Solving the chemical mass balance problem using an artificial neural network. Environmental Science & Technology, 30, 531–535. https://doi.org/10.1021/es950281o
  • Türk, B. (2022). Important factors affecting the quality of indoor air and a bibliometric analysis. Sakarya University Journal of Science, 26(3), 608–619. https://doi.org/10.16984/saufenbilder.996443
  • Uluocak, I., Pinar, E., & Bilgili, M. (2024). Atmospheric NO2 concentration prediction with statistical and hybrid deep learning methods. Environmental and Ecological Statistics, 32(1), 89–118. https://doi.org/10.1007/s10651-024-00637-3
  • Vidal, M. S., Menon, R., Yu, G. F. B., & Amosco, M. D. (2022). Environmental toxicants and preterm birth: a bibliometric analysis of research trends and output. International Journal of Environmental Research and Public Health, 19(5), Article 2493. https://doi.org/10.3390/ijerph19052493
  • Villacura, L., Sánchez, L. F., Catalán, F., Toro A, R., & Leiva, G. M. A. (2024). An overview of air pollution research in Chile: bibliometric analysis and scoping review, challenger and future directions. Heliyon, 10(3), Article e25431. https://doi.org/10.1016/j.heliyon.2024.e25431
  • World Health Organization. (2021a). Air pollution causes 13 deaths per minute worldwide. https://www.who.int/multi-media/details/air-pollution-climate-change
  • World Health Organization. (2021b). COP26 special report on climate change and health. https://www.who.int/publications/i/item/ 9789240036727
  • Yildirim, G., Rahman, A., & Singh, V. P. (2022). A bibliometric analysis of drought indices, risk, and forecast as components of drought early warning systems. Water, 14(2), Article 253. https://doi.org/10.3390/w14020253
  • Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2015). Comparing keywords plus of WOS and author keywords: a case study of patient adherence research. Journal of the Association for Information Science and Technology. 67(4), 967972. https://doi.org/https://doi.org/10.1002/asi.23437
  • Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & Subramanian, R. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11, 291–313. https://doi.org/10.5194/amt-11-291-2018
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Hava Kirliliği Modellemesi ve Kontrolü
Bölüm Araştırma Makalesi
Yazarlar

Olgu Aydın 0000-0001-8220-6384

Hatice Kılar 0000-0002-2423-4712

Proje Numarası Yok
Gönderilme Tarihi 27 Ocak 2025
Kabul Tarihi 8 Nisan 2025
Yayımlanma Tarihi 27 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA Aydın, O., & Kılar, H. (2025). Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme. Doğal Afetler ve Çevre Dergisi, 11(2), 471-487. https://doi.org/10.21324/dacd.1628030
AMA Aydın O, Kılar H. Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme. Doğ Afet Çev Derg. Temmuz 2025;11(2):471-487. doi:10.21324/dacd.1628030
Chicago Aydın, Olgu, ve Hatice Kılar. “Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme”. Doğal Afetler ve Çevre Dergisi 11, sy. 2 (Temmuz 2025): 471-87. https://doi.org/10.21324/dacd.1628030.
EndNote Aydın O, Kılar H (01 Temmuz 2025) Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme. Doğal Afetler ve Çevre Dergisi 11 2 471–487.
IEEE O. Aydın ve H. Kılar, “Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme”, Doğ Afet Çev Derg, c. 11, sy. 2, ss. 471–487, 2025, doi: 10.21324/dacd.1628030.
ISNAD Aydın, Olgu - Kılar, Hatice. “Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme”. Doğal Afetler ve Çevre Dergisi 11/2 (Temmuz2025), 471-487. https://doi.org/10.21324/dacd.1628030.
JAMA Aydın O, Kılar H. Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme. Doğ Afet Çev Derg. 2025;11:471–487.
MLA Aydın, Olgu ve Hatice Kılar. “Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme”. Doğal Afetler ve Çevre Dergisi, c. 11, sy. 2, 2025, ss. 471-87, doi:10.21324/dacd.1628030.
Vancouver Aydın O, Kılar H. Yapay Zekâ Tabanlı Yöntemlerle Hava Kirliliği Araştırmalarının Gelişimi ve Gelecek Perspektifleri: Bibliyometrik Bir İnceleme. Doğ Afet Çev Derg. 2025;11(2):471-87.

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