PRIMENA METODE VEŠTAČKE INTELIGENCIJE U PROCENI KVALITETA POVRŠINSKIH VODA

VODA (2024),  (str. 9-14)

АУТОР(И) / AUTHOR(S): Krtolica Ivana

Download Full Pdf   

DOI: 10.46793/VODA24.009K

САЖЕТАК / ABSTRACT:

U radu je prikazan pregled naučnih publikacija i opisano aktuelno stanje u oblasti na temu primene modela veštačke inteligencije za procenu kvaliteta površinskih voda. Rapidan razvoj modela zasnovanih na principima veštačke inteligencije i njihova visoka tačnost procene uslovile su sve frekventniju primenu ovih modela u oblasti upravljanja i proceni kvaliteta voda. Sposobnost modela veštačkih neuronskih mreža da obrađuju podatke koji nemaju međusobnu linearnu zavisnost, što je karakteristično za korelaciju bioloških i hemijskih parametara, i mogućnost obrade obimnih setova podataka, ove modele čini superiornijim u odnosu na druge i omogućava kvalitetniji monitoring površinskih voda.

КЉУЧНЕ РЕЧИ / KEYWORDS:

veštačka inteligencija, monitoring površinskih voda, biološki i hemijski parametri

ЛИТЕРАТУРА / REFERENCES:

  1. Directive, W. F. (2000). EU Water framework directive. EC Directive, 60.
  2. Piwowarska, D., & Kiedrzyńska, E. (2022). Xenobiotics as a contemporary threat to surface waters. Ecohydrology & Hydrobiology, 22(2), 337-354.;
  3. Tovar-Sánchez, E., Hernández-Plata, I., Martinez, M. S., Valencia-Cuevas, L., & Galante, P. M. (2018). Heavy metal pollution as a biodiversity threat. Heavy Metals, 383;
  4. Sousa, J. C., Ribeiro, A. R., Barbosa, M. O., Pereira, M. F. R., & Silva, A. M. (2018). A review on environmental monitoring of water organic pollutants identified by EU guidelines. Journal of hazardous materials, 344, 146-162.
  5. Elosegi, A., Díez, J., & Mutz, M. (2010). Effects of hydromorphological integrity on biodiversity and functioning of river ecosystems. Hydrobiologia, 657, 199-215.
  6. Palani, S., Liong, S. Y., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine pollution bulletin, 56(9), 1586-1597.
  7. Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological modelling, 220(6), 888-895.
  8. Ibrić, S., Knežević, M., Parojčić, J., & Đurić, Z. (2007). Primena veštačkih neuronskih mreža u formulaciji farmaceutskih preparata. Arhiv za farmaciju, 57(6), 399-414.
  9. Yetilmezsoy, K., Ozkaya, B., & Cakmakci, M. (2011). Artificial intelligence-based prediction models for environmental engineering. Neural Network World, 21(3).
  10. Lek, S., & Guégan, J. F. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological modelling, 120(2-3), 65-73.
  11. Chen, W. B., & Liu, W. C. (2014). Artificial neural network modeling of dissolved oxygen in reservoir. Environmental Monitoring and Assessment, 186, 1203-1217.; Zhu, S., & Heddam, S. (2020). Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN). Water Quality Research Journal, 55(1), 106-118.;
  12. Wen, X., Fang, J., Diao, M., & Zhang, C. (2013). Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environmental monitoring and assessment, 185, 4361-4371.;
  13. Antanasijević, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., & Ristić, M. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20, 9006-9013.
  14. Krtolica, I., Cvijanović, D., Obradović, Đ., Novković, M., Milošević, D., Savić, D., … & Radulović, S. (2021). Water quality and macrophytes in the Danube River: Artificial neural network modelling. Ecological Indicators, 121, 107076.
  15. Tung, T. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 124670
  16. Ighalo, J. O., Adeniyi, A. G., & Marques, G. (2021). Artificial intelligence for surface water quality monitoring and assessment: A systematic literature analysis. Modeling Earth Systems and Environment, 7(2), 669-681.
  17. Irwan, D., Ali, M., Ahmed, A. N., Jacky, G., Nurhakim, A., Ping Han, M. C., … & El-Shafie, A. (2023). Predicting water quality with artificial intelligence: A review of methods and applications. Archives of Computational Methods in Engineering, 30(8), 4633-4652.
  18. Peng, L., Wu, H., Gao, M., Yi, H., Xiong, Q., Yang, L., & Cheng, S. (2022). TLT: Recurrent fine-tuning transfer learning for water quality long-term prediction. Water Research, 225, 119171.;
  19. Nair, J. P., & Vijaya, M. S. (2023). Temporal fusion transformer: A deep learning approach for modeling and forecasting river water quality index. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 277-293.
  20. Liao, Y., Xu, J., & Wang, W. (2011). A method of water quality assessment based on biomonitoring and multiclass support vector machine. Procedia Environmental Sciences, 10, 451-457.