Revisão Sistemática em Sensoriamento de Qualidade de Ar via Lógica Fuzzy e Aprendizado de Máquina
Resumo
Este trabalho propõe uma revisão sistemática da literatura sobre o estado da arte da pesquisa de sensores de qualidade do ar. Este estudo pretende analisar metodologias que integram técnicas de aprendizado de máquina e algoritmos fuzzy para calibração e predição de sensores, tentando demonstrar as principais diferenças e vantagens das abordagens. Uma das principais preocupações deste trabalho é destacar as técnicas e algoritmos mais utilizados, bem como elucidar em que contexto cada um destes métodos melhora as aplicações, visando alcançar um melhor entendimento desta área.
Palavras-chave:
qualidade de ar, aprendizado de máquina, lógica fuzzy, validação de sensores, revisão sistemática
Referências
Alhasa, K. M., Mohd Nadzir, M. S., Olalekan, P., Latif, M. T., Yusup, Y., Iqbal Faruque, M. R., Ahamad, F., Abd. Hamid, H. H., Aiyub, K., Md Ali, S. H., et al. (2018). Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system. Sensors, 18(12):4380.
Bhardwaj, R. and Pruthi, D. (2020). Evolutionary techniques for optimizing air quality model. Procedia Computer Science, 167:1872–1879.
Campbell, J. B. and Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.
Chen, L., Ding, Y., Lyu, D., Liu, X., and Long, H. (2019). Deep multi-task learning based urban air quality index modelling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(1):1–17.
Ferreira, W. d. A. P., Grout, I., and da Silva, A. C. R. (2022). Application of a fuzzy artmap neural network for indoor air quality prediction. In 2022 International Electrical Engineering Congress (iEECON), pages 1–4. IEEE.
Galvão, M. C. B. and Ricarte, I. L. M. (2019). Revisão sistemática da literatura: conceituação, produção e publicação. Logeion: Filosofia da informação, 6(1):57–73.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE.
Kerschen, G., De Boe, P., Golinval, J.-C., andWorden, K. (2004). Sensor validation using principal component analysis. Smart materials and structures, 14(1):36.
Kumar, R., Kumar, P., and Kumar, Y. (2020). Time series data prediction using iot and machine learning technique. Procedia computer science, 167:373–381.
Kumari, C. U., Samiappan, D., Kumar, R., and Sudhakar, T. (2019). Fiber optic sensors in ocean observation: A comprehensive review. Optik, 179:351–360.
Li, H., Monti, A., and Ponci, F. (2012). A fuzzy-based sensor validation strategy for ac motor drives. IEEE Transactions on Industrial Informatics, 8(4):839–848.
Organization, W. H. (2016). Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization.
Pandharipande, A. and Caicedo, D. (2015). Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches. Energy and Buildings, 104:369–377.
Samal, K. K. R., Babu, K. S., Das, S. K., and Acharaya, A. (2019). Time series based air pollution forecasting using sarima and prophet model. In proceedings of the 2019 international conference on information technology and computer communications, pages 80–85.
Upadhyaya, B. R. and Eryurek, E. (1992). Application of neural networks for sensor validation and plant monitoring. Nuclear Technology, 97(2):170–176.
Wang, B., Kong, W., and Guan, H. (2019). Air quality forcasting based on gated recurrent long short-term memory model. In Proceedings of the ACM Turing Celebration Conference-China, pages 1–9.
Wang, B., Yan, Z., Lu, J., Zhang, G., and Li, T. (2018). Deep multi-task learning for air quality prediction. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part V 25, pages 93–103. Springer.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature climate change, 3(10):875–883.
Zhang, H. and Srinivasan, R. (2020). A systematic review of air quality sensors, guidelines, and measurement studies for indoor air quality management. Sustainability, 12(21):9045.
Bhardwaj, R. and Pruthi, D. (2020). Evolutionary techniques for optimizing air quality model. Procedia Computer Science, 167:1872–1879.
Campbell, J. B. and Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.
Chen, L., Ding, Y., Lyu, D., Liu, X., and Long, H. (2019). Deep multi-task learning based urban air quality index modelling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(1):1–17.
Ferreira, W. d. A. P., Grout, I., and da Silva, A. C. R. (2022). Application of a fuzzy artmap neural network for indoor air quality prediction. In 2022 International Electrical Engineering Congress (iEECON), pages 1–4. IEEE.
Galvão, M. C. B. and Ricarte, I. L. M. (2019). Revisão sistemática da literatura: conceituação, produção e publicação. Logeion: Filosofia da informação, 6(1):57–73.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE.
Kerschen, G., De Boe, P., Golinval, J.-C., andWorden, K. (2004). Sensor validation using principal component analysis. Smart materials and structures, 14(1):36.
Kumar, R., Kumar, P., and Kumar, Y. (2020). Time series data prediction using iot and machine learning technique. Procedia computer science, 167:373–381.
Kumari, C. U., Samiappan, D., Kumar, R., and Sudhakar, T. (2019). Fiber optic sensors in ocean observation: A comprehensive review. Optik, 179:351–360.
Li, H., Monti, A., and Ponci, F. (2012). A fuzzy-based sensor validation strategy for ac motor drives. IEEE Transactions on Industrial Informatics, 8(4):839–848.
Organization, W. H. (2016). Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization.
Pandharipande, A. and Caicedo, D. (2015). Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches. Energy and Buildings, 104:369–377.
Samal, K. K. R., Babu, K. S., Das, S. K., and Acharaya, A. (2019). Time series based air pollution forecasting using sarima and prophet model. In proceedings of the 2019 international conference on information technology and computer communications, pages 80–85.
Upadhyaya, B. R. and Eryurek, E. (1992). Application of neural networks for sensor validation and plant monitoring. Nuclear Technology, 97(2):170–176.
Wang, B., Kong, W., and Guan, H. (2019). Air quality forcasting based on gated recurrent long short-term memory model. In Proceedings of the ACM Turing Celebration Conference-China, pages 1–9.
Wang, B., Yan, Z., Lu, J., Zhang, G., and Li, T. (2018). Deep multi-task learning for air quality prediction. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part V 25, pages 93–103. Springer.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature climate change, 3(10):875–883.
Zhang, H. and Srinivasan, R. (2020). A systematic review of air quality sensors, guidelines, and measurement studies for indoor air quality management. Sustainability, 12(21):9045.
Publicado
09/10/2023
Como Citar
SEIBERT, Vagner A.; BASTOS, Rafael R.; SILVA, Gabriel R.; YAMIN, Adenauer; REISER, Renata H. R.; LUCCA, Giancarlo; SANTOS, Helida.
Revisão Sistemática em Sensoriamento de Qualidade de Ar via Lógica Fuzzy e Aprendizado de Máquina. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 7. , 2023, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 95-102.
DOI: https://doi.org/10.5753/weit.2023.26602.
