KaydedilenlerDerginizi ekleyin

Dergiler / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 2

Artificial Intelligence Based Air Quality Prediction Using IoT Sensor Data

Deniz Özer(ISPARTA UNIVERSITY OF APPLIED SCIENCES, FACULTY OF TECHNOLOGY)0009-0008-0805-7518
Bekir Aksoy(ISPARTA UNIVERSITY OF APPLIED SCIENCES, FACULTY OF TECHNOLOGY, DEPARTMENT OF MECHATRONICS ENGINEERING, MECHATRONICS ENGINEERING PR.)0000-0001-8052-9411
Koray Özsoy(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0001-8663-4466
Hilmi Cenk Bayrakçı(ISPARTA UNIVERSITY OF APPLIED SCIENCES, FACULTY OF TECHNOLOGY)0000-0001-5064-7310
Sayfa
—
Yayın Tarihi
2026-05-01
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The main objective of this research is to evaluate the extent to which the developed models can successfully predict complex environmental relationships.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Continuous air quality monitoring is important for protecting human health and ensuring environmental sustainability.
📊
Temel Bulgular & Veri:For this study, an IoT-based data collection system was designed that integrates environmental sensors to record meteorological data such as temperature, humidity and precipitation, as well as a gas sensor which is sensitive to a range of pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and ozone(O3).
💡
Sonuç & Katkı:The main objective of this research is to evaluate the extent to which the developed models can successfully predict complex environmental relationships.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Machine learning · Air Quality · Internet of Things

Abstract

Continuous air quality monitoring is important for protecting human health and ensuring environmental sustainability. Recent advances in sensor technologies, the Internet of Things (IoT) and artificial intelligence have led to innovative solutions that allow environmental parameters to be monitored and analyzed in real time. For this study, an IoT-based data collection system was designed that integrates environmental sensors to record meteorological data such as temperature, humidity and precipitation, as well as a gas sensor which is sensitive to a range of pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and ozone(O3). Based on the ESP32 microcontroller platform, the system has been used to create artificial intelligence models that can predict air quality with high accuracy. The main objective of this research is to evaluate the extent to which the developed models can successfully predict complex environmental relationships.

🔖

Bu Makaleye Atıf Yap

Özer, D., Aksoy, B., Özsoy, K., Bayrakçı, H. C. (2026). Artificial Intelligence Based Air Quality Prediction Using IoT Sensor Data. Turkish Journal of Engineering, Volume: 10 Issue: 2. https://doi.org/10.31127/tuje.1808569

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (53)

  1. Aslantaş, N., Bakırman, T., Selbesoğlu, M. O., & Bayram, B. (2025). The role of ensemble deep learning for building extraction from VHR imagery. International Journal of Engineering and Geosciences, 10(3), 352-363. https://doi.org/10.26833/ijeg.1587798
  2. Dokuz, Y., Bozdağ, A., & Gökçek, B. (2020). Hava kalitesi parametrelerinin tahmini ve mekansal dağılımı için makine öğrenmesi yöntemlerinin kullanılması. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9(1), 37-47. https://doi.org/10.28948/ngumuh.654092
  3. Mohamed, H., Hassan, A., & Elhag, A. (2025). A five-year study using Sentinel-5P data observing seasonal dynamics and long-term trends of atmospheric pollutants. International Journal of Engineering and Geosciences, 10(2), 262-271. https://doi.org/10.26833/ijeg.1587122
  4. Kotan, B., & Erener, A. (2023). Seasonal analysis and mapping of air pollution (PM10 and SO2) during Covid-19 lockdown in Kocaeli (Türkiye). International Journal of Engineering and Geosciences, 8(2),173-187. https://doi.org/10.26833/ijeg.1111699
  5. Zoran, M. A., Savastru, R. S., Savastru, D. M., & Tautan, M. N. (2020). Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. The Science of the Total Environment, 738, Article 139825, 1-12. https://doi.org/10.1016/j.scitotenv.2020.139825
  6. Mutlu, N. M., & Atahanlı, E. B. (2024). Veri madenciliği ile hava kalitesi tahmini: İstanbul örneği. Bilişim Teknolojileri Dergisi, 17(3), 139-158. https://doi.org/10.17671/gazibtd.1426942
  7. Başdoğan, G., & Çığ, A. (2016). Ecological-social-economical impacts of vertical gardens in the sustainable city model. Yuzuncu Yil University Journal of Agricultural Sciences, 26(3), 430-438.
  8. Kotan, B., & Erener, A. (2023). PM10, SO2 hava kirleticilerinin çoklu doğrusal regresyon ve yapay sinir ağları ile sezonsal tahmini. Geomatik, 8(2), 163-179. https://doi.org/10.29128/geomatik.1158565
  9. Pekdoğan, T., Udriștioiu, M. T., Yildizhan, H., & Ameen, A. (2024). From local issues to global impacts: Evidence of air pollution for Romania and Turkey. Sensors, 24(4), Article 1320. https://doi.org/10.3390/s24041320
  10. Yang, Y. (2022). IoT-based air pollution monitoring system. Highlights in Science, Engineering and Technology, 17, 299-307. https://doi.org/10.54097/hset.v17i.2619
  11. Üçgün, H., Gömbeci, F., Yüzgeç, U., & Yalçın, N. (2020). IoT tabanlı platform ile gerçek zamanlı iç ortam hava kalitesi izleme sistemi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7(1), 370-381. https://doi.org/10.35193/bseufbd.730919
  12. Dhingra, S., Madda, R. B., Gandomi, A. H., Patan, R., & Daneshmand, M. (2019). Internet of Things mobile–air pollution monitoring system (IoT-Mobair). IEEE Internet of Things Journal, 6(3), 5577-5584. https://doi.org/10.1109/JIOT.2019.2903821
  13. Ece, N. J. (2024). Yapay zeka: Denizcilik sektöründe kullanımı ve swot analizi. Mersin Üniversitesi Denizcilik ve Lojistik Araştırmaları Dergisi, 6(1), 30-51. https://doi.org/10.54410/denlojad.1491372
  14. Pekdoğan, T. (2024). Yapay zekâ tabanlı hava kalitesi iyileştirme stratejilerinin değerlendirilmesi. EMO Bilimsel Dergi, 14(2), 87-97. https://doi.org/10.61512/emobd.1498119
  15. Xue, L., Liu, Y., Xiong, Y., Liu, Y., Cui, X., & Lei, G. (2021). A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science and Engineering, 196, Article 107801, 1-13. https://doi.org/10.1016/j.petrol.2020.107801
  16. Pratama, E. W., & Kiswantono, A. (2022). Electrical analysis using ESP-32 module in realtime. Journal of Electrical Engineering and Computer Sciences, 7(2), 1273–1284. https://doi.org/10.54732/jeecs.v7i2.21
  17. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
  18. Alp, G., & Soygazi, F. (2025). Enhancing crop recommendation with SMOTE-augmented machine learning algorithms. Turkish Journal of Engineering, 9(4), 612-620.
  19. Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79. https://doi.org/10.58496/BJML/2024/007
  20. Özer, D., & Aksoy, B. (2024). Yapay zekâ uygulaması ile güneş paneli sistemi enerji üretimi tahmini. Yalvaç Akademi Dergisi, 9(2), 138-151. https://doi.org/10.57120/yalvac.1543369
  21. Öztürk, A., Allahverdı, N., & Saday, F. (2022). Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering, 6(1), 54-62. https://doi.org/10.31127/tuje.807019
  22. Zhao, Z., Yang, W., Zhai, Y., Liang, Y., & Zhao, Y. (2022). Identify DNA-binding proteins through the extreme gradient boosting algorithm. Frontiers in Genetics, 12, 821996, 1-9. https://doi.org/10.3389/fgene.2021.821996
  23. Vennerød, C. B., Kjærran, A., & Bugge, E. S. (2021). Long short-term memory RNN. ArXiv. https://doi.org/10.48550/arXiv.2105.06756
  24. Safar, A. A., Salih, D. M., & Murshid, A. M. (2023). Pattern recognition using the multi-layer perceptron (MLP) for medical disease: A survey. International Journal of Nonlinear Analysis and Applications, 14(1), 1989-1998. https://doi.org/10.22075/ijnaa.2022.7114
  25. Mohammed, A. J., Arif, M. H., & Ali, A. A. (2020). A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems. IAES International Journal of Artificial Intelligence, 9(4), 609–615. https://doi.org/10.11591/ijai.v9.i4.pp609-615
  26. Ren, Y., Li, X., Yang, X., & Xu, H. (2023). Sea ice detection from SAR images based on deep fully convolutional networks. In X. Li & F. Wang (Eds.), Artificial intelligence oceanography, 253-276. Springer, Singapore. https://doi.org/10.1007/978-981-19-6375-9_12
  27. Maza, D., Ojo, J. O., & Akinlade, G. O. (2024). A predictive machine learning framework for diabetes. Turkish Journal of Engineering, 8(3), 583-592. https://doi.org/10.31127/tuje.1434305
  28. Sinap, V. (2024). Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets. Turkish Journal of Engineering, 8(2), 196-208. https://doi.org/10.31127/tuje.1386127
  29. Biyik, M. Y., Atik, M. E., & Duran, Z. (2023). Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis. International Journal of Engineering and Geosciences, 8(2), 138-145. https://doi.org/10.26833/ijeg.1080624
  30. Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and F1 score for more thorough evaluation of classification models. In S. Eger, Y. Gao, M. Peyrard, W. Zhao, & E. Hovy (Eds.), Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, USA, 79–91. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.eval4nlp-1.9
  31. Rai, T., Morisi, A., Bacci, B., Bacon, N. J., Dark, M. J., Aboellail, … & Wells, K. (2024). Keeping pathologists in the loop and an adaptive F1-score threshold method for mitosis detection in canine perivascular wall tumours. Cancers, 16(3), Article 644, 1-20. https://doi.org/10.3390/cancers16030644
  32. Celik, F., & Çelik, K. (2025). Prediction of land image classification using squeeze synchronization layer and convolve craft focus module in ResNet 101 model. International Journal of Engineering and Geosciences, 10(2), 197-206. https://doi.org/10.26833/ijeg.1538708
  33. Bloch, L., & Friedrich, C. M. (2022). Machine learning workflow to explain black-box models for early Alzheimer’s disease classification evaluated for multiple datasets. SN Computer Science, 3, Article 509, 1-38. https://doi.org/10.1007/s42979-022-01371-y
  34. Luo, Y., Wang, Z., & Wang, C. (2021). Improvement of APACHE II score system for disease severity based on XGBoost algorithm. BMC Medical Informatics and Decision Making, 21(1), Article 237, 1-12. https://doi.org/10.1186/s12911-021-01591-x
  35. Yang, Y., Wang, K., Yuan, Z., & Liu, D. (2022). Predicting freeway traffic crash severity using XGBoost-Bayesian network model with consideration of features interaction. Journal of Advanced Transportation, 2022, Article 4257865, 1-16. https://doi.org/10.1155/2022/4257865
  36. Yasper, A., Handoko, D., Putra, M., Aliwarga, H. K., & Rosid, M. S. R. (2023). Hyperparameters optimization in XGBoost model for rainfall estimation: A case study in Pontianak City. Jurnal Penelitian Pendidikan IPA, 9(9), 7113–7121. https://doi.org/10.29303/jppipa.v9i9.3890
  37. Raymaekers, J., Rousseeuw, P. J., Servotte, T., Verdonck, T., & Yao, R. (2025). A powerful random forest featuring linear extensions (RAFFLE). ArXiv. https://doi.org/10.48550/arXiv.2502.10185
  38. Kitcharanant, N., Chotiyarnwong, P., Tanphiriyakun, T., Vanitcharoenkul, E., Mahaisavariya, C., Boonyaprapa, W., & Unnanuntana, A. (2022). Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture. BMC Geriatrics, 22(1), Article 451, 1-15. https://doi.org/10.1186/s12877-022-03152-x
  39. Sumathi, B. (2020). Grid search tuning of hyperparameters in random forest classifier for customer feedback sentiment prediction. International Journal of Advanced Computer Science and Applications, 11(9), 377–383. https://dx.doi.org/10.14569/IJACSA.2020.0110920
  40. Nti, I. K., Nyarko-Boateng, O., & Aning, J. (2021). Performance of machine learning algorithms with different K values in K-fold cross-validation. International Journal of Information Technology and Computer Science, 13(6), 61-71. https://doi.org/10.5815/ijitcs.2021.06.05
  41. Durap, A. (2025). Machine learning-based wind speed prediction using random forest: A cross-validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508-518. https://doi.org/10.31127/tuje.1624354
  42. Murugan, R., & Palanichamy, N. (2021). Smart city air quality prediction using machine learning. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 1048-1054. https://doi.org/10.1109/ICICCS51141.2021.9432074
  43. Ali, S., Coleman, S., Kerr, D., & Quinn, J. (2025). Predictive analytics of air quality for IoT-enabled industrial environments. 2025 IEEE Symposium on Computational Intelligence on Engineering/Cyber Physical Systems Companion (CIES Companion), 1–5. https://doi.org/10.1109/CIESCompanion65073.2025.11010902
  44. Liu, Q., Cui, B., & Liu, Z. (2024). Air quality class prediction using machine learning methods based on monitoring data and secondary modeling. Atmosphere, 15(5), 553. https://doi.org/10.3390/atmos15050553
  45. Tasmurzayev, N., Amangeldy, B., Smagulova, G., Baigarayeva, Z., & Imash, A. (2025). A low-cost IoT sensor and preliminary machine-learning feasibility study for monitoring in-cabin air quality: A pilot case from Almaty. Sensors, 25(14), Article 4521, 1-31. https://doi.org/10.3390/s25144521
  46. Islam, M. M., Jibon, F. A., Tarek, M. M., Kanchan, M. H., & Shakil, S. U. P. (2024). A real-time dataset of air quality index monitoring using IoT and machine learning in the perspective of Bangladesh. Data in Brief, 55, Article 110578, 1-9. https://doi.org/10.1016/j.dib.2024.110578
  47. Sudipa, I. G. I., Habibi, M., Atmadji, E. S. J., & Arfiani, I. (2024). Predictive modeling of air quality levels using decision tree classification: Insights from environmental and demographic factors. Indonesian Journal of Data and Science, 5(3), 251–258.
  48. Mumtaz, R., Zaidi, S. M. H., Shakir, M. Z., Shafi, U., Malik, M. M., Haque, A.,. & Zaidi, S. A. R. (2021). Internet of Things (IoT) based indoor air quality sensing and predictive analytic—A COVID-19 perspective. Electronics, 10(2), 184. https://doi.org/10.3390/electronics10020184
  49. Baqer, N. S., Albahri, A. S., Mohammed, H. A., Albahri, O. S., Zaidan, A. A., Zaidan, B. B., … & Al-Ani, R. K. (2022). Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques. Telecommunication Systems, 81, 591–613. https://doi.org/10.1007/s11235-022-00959-2
  50. Rajanala, V. P. (2025). Air Quality Prediction Using Machine Learning Models. SGS-Engineering & Sciences, 1(2).
  51. Jahan, N., Mehedi, C. M., Arefin, M. S., Uddin, M. B., Delowar, K. E., & Reza, A. W. (2025). Discovering patterns in environmental data for air quality analysis using association rule mining. 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), Rangpur, Bangladesh, 1–6. https://doi.org/10.1109/QPAIN66474.2025.11172234.
  52. Murugan, R., & Palanichamy, N. (2021). Smart city air quality prediction using machine learning. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 1048-1054. https://doi.org/10.1109/ICICCS51141.2021.9432074
  53. Mumtaz, R., Zaidi, S. M. H., Shakir, M. Z., Shafi, U., Malik, M. M., Haque, A.,. & Zaidi, S. A. R. (2021). Internet of Things (IoT) based indoor air quality sensing and predictive analytic—A COVID-19 perspective. Electronics, 10(2), 184. https://doi.org/10.3390/electronics10020184