Have a personal or library account? Click to login
Estimation of Manganese Content in Potable Water by Boosting Techniques Cover

Estimation of Manganese Content in Potable Water by Boosting Techniques

By: M. Göçer,  S. B. Coşkun and  B. Yanık  
Open Access
|Dec 2024

References

  1. Aldhyani, T. H., Al-Yaari, M., Alkahtani, H., and Maashi, M. 2020. Water quality prediction using artificial intelligence algorithms. Applied Bionics and Biomechanics, https://doi.org/10.1155/2020/6659314.
  2. Bui, D. T., Khosravi, K., Tiefenbacher, J., Nguyen, H., and Kazakis, N., 2020. Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Science of the Total Environment, 721, 137612, https://doi.org/10.1016/j.scitotenv.2020.137612.
  3. Canpolat, Ö., and Çalta M., 2001. Keban Baraj Gölü’nden (Elazığ) yakalan Acanthobrama Marmid (Heckel, 1843)’de bazı ağır metal düzeylerinin belirlenmesi (Determination of some heavy metal levels in Acanthobrama Marmid (Heckel, 1843) caught from Keban Dam Lake (Elazığ)). Fırat Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 13(2), pp. 263-268.
  4. Chawla, N. V., Bowyer, K. W., O. Hall, L. O., and Kegelmeyer, W. P., 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, pp. 321-357.
  5. Chawla, N. V., 2010. Data mining for imbalanced datasets: an overview. Data mining and knowledge discovery handbook, pp. 875-886.
  6. Coles, C., Crawford, J., McClure, P. R., Roney, N., and Todd, G. D., 2012. “Toxicological profile for manganese”. Georgia, USA https://www.atsdr.cdc.gov/toxprofiles/tp151.pdf (view at 12 May 2024).
  7. Dobson, A. W., Erikson K.M., Aschner M., 2004. Manganese neurotoxicity. Ann NY Acad Sci, 1012, pp. 115–128.
  8. Dorogush, A. V., Ershov, V., and Gulin, A., 2018. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.
  9. Dönderici, Z. S., Dönderici A., and Bașarı F., 2010. An investigation on physical and chemical quality of spring waters. Türk Hijyen ve Deneysel Biyoloji Dergisi 67(4), pp. 167-172.
  10. Du, J., 2004. Potable Water Health Advisory For Manganese. US Environmental Protection Agency, Washington, pp. 36.
  11. Freund, Y., and Schapire, R. E., 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, Berlin Heidelberg, pp. 23-37.
  12. Friedman, J. H., 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics, pp. 1189-1232.
  13. Holzgraefe, M., Poser, W., Kijewski, H., and Beuche, W., 1986. Chronic enteral poisoning caused by potassium permanganate: a case report. Journal of Toxicology: Clinical Toxicology, 24(3), pp. 235-244.
  14. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W.,Ye, Q., and Liu, T. Y., 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, pp. 3146–3154.
  15. Kearns, M., 1998. Thoughts on hypothesis boosting. Unpublished manuscript, 45, pp. 105.
  16. Kearns, M., and Leslie V., 1994. Cryptographic limitations on learning boolean formulae and finite automata. Journal of the ACM (JACM), 41(1), pp. 67-95.
  17. Khan, M. S. I., Islam, N., Uddin, J., Islam, S., and Nasir, M. K., 2022. Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. Journal of King Saud University-Computer and Information Sciences, 34(8), pp. 4773-4781.
  18. Klaassen, C. D., 2006. Heavy metals and heavy-metal antagonists. Goodman and Gilmans The Pharmacological Basis of Therapeutics. 11th edition, New York: McGraw-Hill, pp. 1753-1775.
  19. Kohl, P. M., and Steven J. M., 2006. Occurrence of manganese in potable water and manganese control. American Water Works Association.
  20. Laurikkala, J., 2001. Improving identification of difficult small classes by balancing class distribution. In: Artificial Intelligence in Medicine: 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portugal, July 14, 2001, Proceedings 8. Springer, Berlin Heidelberg, pp. 63-66.
  21. Liao, H., and Sun, W., 2010. Forecasting and evaluating water quality of Chao Lake based on an improved decision tree method. Procedia Environmental Sciences, 2, pp. 970-979.
  22. Liashchynskyi, P., and Liashchynskyi, P., 2019. Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv preprint arXiv:1912.06059.
  23. Liu, J., Yu, C., Hu, Z., Zhao, Y., Bai, Y., Xie, M., and Luo, J., 2020. Accurate prediction scheme of water quality in smart mariculture with deep Bi-S-SRU learning network. IEEE Access, 8, pp. 24784-24798.
  24. Marmara University, Doğa Bitkileri ve Su Ürünleri Araştırma ve Uygulama Merkezi, 2022. İçme suyu kabul edilebilir değerler (Potable water acceptable values), İstanbul, Türkiye https://dobisu.marmara.edu.tr/orta-menu/yararli-bilgiler/icmesuyu-kabul-edilebilir-degerler (view at 12 May 2024).
  25. Mergler D., Baldwin M., Belanger S., Larribe F., Beuter A., Bowler R., Panisset M., Edwards R., De Geoffroy A., Sassine M. P., Hudnell K., 1999. Manganese neurotoxicity a continuum of dysfunction: results from a com- munity based study. Neurotoxicology, 20(2-3), pp. 327.
  26. Mitchell, R., and Frank, E., 2017. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science 3:e127 https://doi.org/10.7717/peerj-cs.127 (view at 12 May 2024).
  27. Perl, D. P., and Olanow, C. W., 2007. The neuropathology of manganese-induced Parkinsonism. Journal of Neuropathology & Experimental Neurology, 66(8), pp. 675-682.
  28. Samsudin, M. S., Azid, A., Khalit, S. I., Sani, M. S. A., and Lananan, F., 2019. Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones. Marine Pollution Bulletin, 141, pp. 472-481.
  29. Schapire, R. E., 1990. The strength of weak learnability. Machine learning, 5, pp. 197-227.
  30. Shelke, M. S., Deshmukh, P. R., and Shandilya, V. K., 2017. A review on imbalanced data handling using undersampling and oversampling technique. Int. J. Recent Trends Eng. Res, 3(4), pp. 444-449.
  31. Sly, L. I., Hodgkinson, M. C., and Arunpairojana, V., 1990. Deposition of manganese in a potable water distribution system. Applied and environmental microbiology, 56(3), pp- 628-639.
  32. Talaat, H., Montaser Y. G., Eman M. K., Enas M. A., and Awad E. M., 2010. Simultaneous removal iron and manganese from ground water by combined photo-electrochemical method. Journal of American Science, 6(12), pp. 1-7.
  33. Theis, T. L., and Singer, P. C., 1974. Complexation of iron (II) by organic matter and its effect on iron (II) oxygenation. Environmental Science & Technology, 8(6), pp. 569-573.
  34. Tianqi, C., and Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794.
  35. Wang, X., Zhang, F., and Ding, J., 2017. Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China. Scientific reports, 7(1), pp. 12858.
  36. Weiss, G. M., and Provost, F., 2001. The effect of class distribution on classifier learning: an empirical study. Rutgers University, 2001.
  37. Weng, T. Y., Liu, W. Y., and Xiao, J., 2020. Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems, 120(2), pp. 265-279.
  38. World Health Organization, 2002. Guidelines for potable-water quality. World health organization, Geneva, pp. 303-304.
  39. World Health Organization, 2011. Guidelines for potable-water quality. World health organization, Geneva, pp. 31-32.
  40. Yilma, M., Kiflie, Z., Windsperger, A., and Gessese, N., 2018. Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia. Modeling Earth Systems and Environment, 4, pp. 175-187.
  41. İzmit Su A.Ş., 2014. Yuvacık Barajı (Yuvacık Dam), İzmit, Türkiye https://www.izmitsu.com.tr/sayfa.asp?ID=15&PID=2&SID=13 (view at 12 May 2024).
Language: English
Page range: 260 - 267
Submitted on: Jun 29, 2024
Accepted on: Jul 24, 2024
Published on: Dec 10, 2024
Published by: University of Oradea, Civil Engineering and Architecture Faculty
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2024 M. Göçer, S. B. Coşkun, B. Yanık, published by University of Oradea, Civil Engineering and Architecture Faculty
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.