Yapay Zekâ Tabanlı Hava Kalitesi İyileştirme Stratejilerinin Değerlendirilmesi

Year 2024, Volume: 14 Issue: 2, 87 - 97, 30.07.2024

Tuğçe Pekdoğan

Abstract

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.

Keywords

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

Ethical Statement

Bu çalışmada herhangi bir etik ihlal söz konusu olmamıştır.

Evaluation of Artificial Intelligence-Based Air Quality Improvement Strategies

Abstract

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.

Keywords

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

References

• [1] S. Sharma, M. Zhang, Anshika, J. Gao, H. Zhang, S.H. Kota, “Effect of Restricted Emissions During COVID-19 on Air Quality in India,” The Science of the Total Environment, 2020
• [2] K. Balakrishnan, S. Dey, T. Gupta, R.S. Dhaliwal, M. Brauer, A.J. Cohen, J.D. Stanaway, G. Beig, T.K. Joshi, A.N. Aggarwal, Y. Sabde, H. Sadhu, J. Frostad, K. Causey, W. Godwin, D.K. Shukla, G.A. Kumar, C.M. Varghese, P. Muraleedharan, A. Agrawal, R.M. Anjana, , “The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the Global Burden of Disease Study 2017,” The Lancet Planetary Health, vol. 3, no. 1, 2019
• [3] C.A. Pope, D.W. Dockery, “Health effects of fine particulate air pollution: Lines that connect,” Journal of the Air and Waste Management Association, vol. 56, no. 6, 2006
• [4] WHO, “WHO global air quality guidelines: particulate matter (‎PM2.5 and PM10)‎, ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide.” [Online]. Available: https://apps.who.int/iris/handle/10665/345329
• [5] M.A. Zoran, R.S. Savastru, D.M. Savastru, M.N. Tautan, “Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy,” Science of the Total Environment, vol. 738, 2020
• [6] J.O. Anderson, J.G. Thundiyil, A. Stolbach, “Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health,” Journal of Medical Toxicology, vol. 8, no. 2. 2012.
• [7] A. Ratajczak, A. Badyda, P.O. Czechowski, A. Czarnecki, M. Dubrawski, W. Feleszko, “Air pollution increases the incidence of upper respiratory tract symptoms among Polish children,” Journal of Clinical Medicine, vol. 10, no. 10, p. 2150, 2021.
• [8] T. Bourdrel, M.A. Bind, Y. Béjot, O. Morel, J.F. Argacha, “Cardiovascular effects of air pollution,” Archives of Cardiovascular Diseases, vol. 110, no. 11. 2017.
• [9] R.B. Hamanaka, G.M. Mutlu, “Particulate matter air pollution: effects on the cardiovascular system,” Frontiers in endocrinology, vol. 9, p. 680, 2018.
• [10] M. Laeremans, E. Dons, I. Avila-Palencia, G. Carrasco-Turigas, J.P. Orjuela, E. Anaya, T. Cole-Hunter, A. De Nazelle, M. Nieuwenhuijsen, A. Standaert, “Short-term effects of physical activity, air pollution and their interaction on the cardiovascular and respiratory system,” Environment international, vol. 117, pp. 82–90, 2018.
• [11] C. Spix, H.R. Anderson, J. Schwartz, M.A. Vigotti, A. Letertre, J.M. Vonk, G. Touloumi, F. Balducci, T. Piekarski, L. Bacharova, “Short-term effects of air pollution on hospital admissions of respiratory diseases in Europe: a quantitative summary of APHEA study results,” Archives of Environmental Health: An International Journal, vol. 53, no. 1, pp. 54–64, 1998.
• [12] M. Kampa, E. Castanas, “Human health effects of air pollution,” Environmental pollution, vol. 151, no. 2, pp. 362–367, 2008.
• [13] L. Schinasi, R.A. Horton, V.T. Guidry, S. Wing, S.W. Marshall, K.B. Morland, “Air pollution, lung function, and physical symptoms in communities near concentrated swine feeding operations,” Epidemiology, vol. 22, no. 2, 2011
• [14] A.J. Elliot, S. Smith, A. Dobney, J. Thornes, G.E. Smith, S. Vardoulakis, “Monitoring the effect of air pollution episodes on health care consultations and ambulance call-outs in England during March/April 2014: A retrospective observational analysis,” Environmental pollution, vol. 214, pp. 903–911, 2016.
• [15] Y. Xue, L. Wang, Y. Zhang, Y. Zhao, Y. Liu, “Air pollution: A culprit of lung cancer,” Journal of Hazardous Materials, vol. 434. 2022.
• [16] T. Pekdogan, M.T. Udriștioiu, H. Yildizhan, A. Ameen, “From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey,” Sensors, vol. 24, no. 4, p. 1320, 2024.
• [17] G. Başdoğan, Ç. Arzu, “Ecological-social-economical impacts of vertical gardens in the sustainable city model,” Yuzuncu Yıl University Journal of Agricultural Sciences, vol. 26, no. 3, pp. 430–438, 2016.
• [18] X. Lu, S. Zhang, J. Xing, Y. Wang, W. Chen, D. Ding, Y. Wu, S. Wang, L. Duan, J. Hao, “Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era,” Engineering, vol. 6, no. 12. 2020.
• [19] M. Angelidou, A. Psaltoglou, N. Komninos, C. Kakderi, P. Tsarchopoulos, A. Panori, “Enhancing sustainable urban development through smart city applications,” Journal of Science and Technology Policy Management, 2018
• [20] L. García, A.J. Garcia-Sanchez, R. Asorey-Cacheda, J. Garcia-Haro, C.L. Zúñiga-Cañón, “Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments,” Sensors, vol. 22, no. 23, 2022
• [21] Y. Yang, “IoT-based air pollution monitoring system,” Highlights in Science, Engineering and Technology, vol. 17, pp. 299–307, Nov.2022
• [22] Ö. Zeydan, M. Pekkaya, “Evaluating air quality monitoring stations in Turkey by using multi criteria decision making,” Atmospheric Pollution Research, vol. 12, no. 5, p. 101046, 2021.
• [23] A. Suleiman, M.R. Tight, A.D. Quinn, “Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2.5),” Atmospheric Pollution Research, vol. 10, no. 1, 2019
• [24] M. Taştan, “A low-cost air quality monitoring system based on Internet of Things for smart homes,” Journal of Ambient Intelligence and Smart Environments, vol. 14, no. 5, 2022
• [25] A. Gacar, H. Aktas, B. Ozdogan, “Digital agriculture practices in the context of agriculture 4.0,” Pressacademia, vol. 4, no. 2, 2017
• [26] M.S. Farooq, S. Riaz, A. Abid, T. Umer, Y. Bin Zikria, “Role of iot technology in agriculture: A systematic literature review,” Electronics (Switzerland), vol. 9, no. 2. 2020.
• [27] B. Bahmei, E. Birmingham, S. Arzanpour, “CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification,” IEEE Signal Processing Letters, vol. 29, 2022
• [28] X. Li, L. Peng, X. Yao, S. Cui, Y. Hu, C. You, T. Chi, “Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation,” Environmental Pollution, vol. 231, 2017
• [29] P.W. Tien, S. Wei, J. Darkwa, C. Wood, J.K. Calautit, “Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review,” Energy and AI, vol. 10. 2022.
• [30] S.C. Sofuoglu, A. Sofuoglu, S. Birgili, G. Tayfur, “Forecasting ambient air SO2 concentrations using artificial neural networks,” Energy Sources, Part B: Economics, Planning and Policy, vol. 1, no. 2, 2006
• [31] R.S. Suri, A.K. Jain, N.R. Kapoor, A. Kumar, H.C. Arora, K. Kumar, H. Jahangir, “Air Quality Prediction-A Study Using Neural Network Based Approach,” Journal of Soft Computing in Civil Engineering, vol. 7, no. 1, pp. 93–113, Jan.2023
• [32] A.R. Alsaber, J. Pan, A. Al-Hurban, “Handling complex missing data using random forest approach for an air quality monitoring dataset: A case study of kuwait environmental data (2012 to 2018),” International Journal of Environmental Research and Public Health, vol. 18, no. 3, pp. 1–26, Feb.2021
• [33] C.C. Liu, T.C. Lin, K.Y. Yuan, P. Te Chiueh, “Spatio-temporal prediction and factor identification of urban air quality using support vector machine,” Urban Climate, vol. 41, Jan.2022
• [34] M.A. Rafif, G. Sanjaya Indrajaya, M.K. Al-Ghazi, J. Johnny, N.T.M. Sagala, “Comparison of Decision Tree and Support Vector Machine for Predicting Jakarta Air Quality Index,” in ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 381–385.
• [35] K. Kumar, B.P. Pande, “Air pollution prediction with machine learning: a case study of Indian cities,” International Journal of Environmental Science and Technology, vol. 20, no. 5, pp. 5333–5348, May2023
• [36] W.-T. Tsai, Y.-Q. Lin, “Trend Analysis of Air Quality Index (AQI) and Greenhouse Gas (GHG) Emissions in Taiwan and Their Regulatory Countermeasures,” Environments, 2021
• [37] J. Toutouh, S. Nesmachnow, D.G. Rossit, “Generative adversarial networks to model air pollution under uncertainty,” in CEUR Workshop Proceedings, 2021.
• [38] V. Athira, P. Geetha, R. Vinayakumar, K.P. Soman, “DeepAirNet: Applying Recurrent Networks for Air Quality Prediction,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 1394–1403.
• [39] N. Zimmerman, A.A. Presto, S.P.N. Kumar, J. Gu, A. Hauryliuk, E.S. Robinson, A.L. Robinson, R. Subramanian, “A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring,” Atmospheric Measurement Techniques, vol. 11, no. 1, pp. 291–313, 2018
• [40] R. Yu, Y. Yang, L. Yang, G. Han, O.A. Move, “RAQ–A random forest approach for predicting air quality in urban sensing systems,” Sensors (Switzerland), vol. 16, no. 1, Jan.2016
• [41] K. Zhang, J. Yang, J. Sha, H. Liu, “Dynamic slow feature analysis and random forest for subway indoor air quality modeling,” Building and Environment, vol. 213, 2022
• [42] A. Moradibaad, R. Mashhoud, Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks. 2018.
• [43] M. Gao, L. Yin, J. Ning, “Artificial neural network model for ozone concentration estimation and Monte Carlo analysis,” Atmospheric Environment, vol. 184, 2018
• [44] J. Ordieres-Meré, E. Vergara, S. Capuz-Rizo, R. Salazar, “Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua),” Environmental Modelling & Software, vol. 20, pp. 547–559, May2005
• [45] M.J. Moradi, M.A. Hariri-Ardebili, “Developing a library of shear walls database and the neural network based predictive meta-model,” Applied Sciences, vol. 9, no. 12, p. 2562, 2019.
• [46] Z. Bai, C. Peng, “Convolutional Neural Network (CNN) Supported Urban Design to Reduce Particle Air Pollutant Concentrations,” in Proceedings of the 28th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1], 2023.
• [47] Y. Mao, S. Lee, “Deep Convolutional Neural Network for Air Quality Prediction,” in Journal of Physics: Conference Series, 2019.
• [48] E. Akın, M.E. Şahin, “Derin Öğrenme ve Yapay Sinir Ağı Modelleri Üzerine Bir İnceleme,” EMO Bilimsel Dergi, vol. 14, no. 1, pp. 27–38, 2024 [Online]. Available: https://dergipark.org.tr/tr/pub/emobd/issue/83029/1338066
• [49] S. Ramachandraarjunan, V. Perumalsamy, B. Narayanan, “IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques,” Journal of Intelligent and Fuzzy Systems, vol. 43, no. 3, 2022
• [50] H. Nurcahyanto, A.T. Prihatno, M.M. Alam, M.H. Rahman, I. Jahan, M. Shahjalal, Y.M. Jang, “Multilevel RNN-Based PM10 Air Quality Prediction for Industrial Internet of Things Applications in Cleanroom Environment,” Wireless Communications and Mobile Computing, vol. 2022, 2022
• [51] X. Zhao, R. Zhang, J.L. Wu, P.C. Chang, “A deep recurrent neural network for air quality classification,” Journal of Information Hiding and Multimedia Signal Processing, vol. 9, no. 2, pp. 346–354, Mar.2018.
• [52] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems, vol. 28, 2015.
• [53] S.J. Livingston, S.D. Kanmani, A.S. Ebenezer, D. Sam, A. Joshi, “An ensembled method for air quality monitoring and control using machine learning,” Measurement: Sensors, vol. 30, 2023
• [54] K. Gaurav, B.K. Singh, V. Kumar, “Intelligent fault monitoring and reliability analysis in safety–critical systems of nuclear power plants using SIAO-CNN-ORNN,” Multimedia Tools and Applications, 2024
• [55] Z.S. Asaei-Moamam, F. Safi-Esfahani, S. Mirjalili, R. Mohammadpour, M.H. Nadimi-Shahraki, “Air quality particulate-pollution prediction applying GAN network and the Neural Turing Machine,” Applied Soft Computing, vol. 147, 2023
• [56] A.N. Wu, R. Stouffs, F. Biljecki, “Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales,” Building and Environment, vol. 223. 2022.
• [57] S. Sarwar, G. Aziz, D. Balsalobre-Lorente, “Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia,” Sustainability, vol. 15, no. 20, 2023
• [58] W. Huang, T. Li, J. Liu, P. Xie, S. Du, F. Teng, “An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability,” Information Fusion, vol. 75. 2021.
• [59] M. Méndez, M.G. Merayo, M. Núñez, “Machine learning algorithms to forecast air quality: a survey,” Artificial Intelligence Review, vol. 56, no. 9, 2023