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A Case Study on the Relationship between Water Quality Parameters: Bursa

Monitoring the quality of mains water in residential areas where industrialization is intense is of vital importance in terms of human health. For this purpose, quality parameters expressing the physical, chemical and biological properties of water are periodically observed through laboratory tests. During the evaluation of water quality, these parameters can be assessed individually or as a group by considering their interrelations. In this context, by using water quality reports of Bursa province which is an industrial city, answers to two questions were sought. The first of these questions is, getting evaluated on a group basis, which groups of water quality parameters are found to be highly correlated. The second question is whether the correlation between these interrelated parameter groups can be maintained in different measurement periods. For these purposes, analyzes were made using an approach which utilizes canonical correlation analysis, exhaustive scanning, and sliding window methods. As a result of these analyzes, it was observed that used approach gave successful results in terms of determining interrelated parameter groups and the differences in terms of interrelations between the measurement periods over these groups.

Keywords:

water quality parameters, canonical correlation analysis, exhaustive search sliding window,

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[1] H. H. Mitchell, T. S. Hamilton, F. R. Steggerda, H. W. Bean, “The chemical composition of the adult human body and its bearing on the biochemistry of growth,” in The Journal of Biological Chemistry, 158, pp. 625-637, 1945.
[2] R. Noori, M. S. Sabahi, A. R. Karbassi, A. Baghvand, H. Taati Zadeh, “Multivariate statistical analysis of surface water quality based on correlations and variations in the data set,” in Desalination, 260, pp. 129-136, 2010.
[3] M. C. Chan, I. Lou, W. K. Ung, K. M. Mok, “Integrating principle component analysis and canonical correlation analysis for monitoring water quality in storage reservoir,” in Applied Mechanics and Materials, 284-287, pp. 1458-1462, 2013.
[4] K. S. Parmar, R. Bhardwaj, “Wavelet and statistical analysis of river water quality parameters,” in Applied Mathematics and Computation, 219, pp. 10172-10182, 2013.
[5] G. A. H. Sallam, E. A. Elsayed, “Estimating relations between temperature, relative humidity as independent variables and selected water quality parameters in Lake Manzala, Egypt,” in Ain Shams Engineering Journal, 9, pp. 1-14, 2018.
[6] E. Dogan, B. Sengorur, R. Koklu, “Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique,” in Journal of Environmental Management, 90, pp. 1229-1235, 2009.
[7] M. J. Alizadeh, M. R. Kavianpour, “Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean,” in Marine Pollution Bulletin, 98, pp. 171-178, 2015.
[8] I. Seo, S. H. Yun, S. Y. Choi, “Forecasting water quality parameters by ANN model using preprocessing technique at the downstream of Cheongpyeong dam,” in Procedia Engineering, 154, pp. 1110-1115, 2016.
[9] S. Mazhar, A. Ditta, L. Bulgariu, I. Ahmad, M. Ahmed, A. A. Nadiri, “Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani fuzzy logic model and phytotoxicity assessment,” in Chemosphere, 227, pp. 256-268, 2019.
[10] G. A. Cordoba, L. Tuhovcak, M. Taus, “Using artificial neural network models to assess water quality in water distribution networks,” in Procedia Engineering, 70, pp. 399-408, 2014.
[11] A. D. Sutadian, N. Muttil, A. G. Yilmaz, B. J. C. Perera, “Using the analytic hierarchy process to identify parameter weights for developing a water quality index,” in Ecological Indicators, 75, pp. 220-233, 2017.
[12] G. Sotomayor, H. Hampel, R. F. Vazquez, “Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm,” in Water Research, 130, pp. 353-362, 2018.
[13] A. N. Ahmed, F. B. Othman, H. A. Afan, R. K. Ibrahim, C. M. Fai, M. S. Hossain, M. Ehteram, A. Elshafie, “Machine learning methods for better water quality prediction,” in Journal of Hydrology, 578, 124084, 2019.
[14] M. Tripathi, S .K. Singal, “Use of principal component analysis for parameter selection for development of a novel water quality index: A case study of river Ganga India,” in Ecological Indicators, 96, pp. 430-436, 2019.
[15] D. R. Hardoon, S. Szedmak, J. S. Taylor, “Canonical Correlation Analysis: An overview with application to learning methods,” in Neural Computation, 16(12), pp. 2639-2664, 2004.
[16] C. O. Sakar, O. Kursun, F. Gurgen, “A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method,” in Expert Systems with Applications, 39(3), pp. 3432-3437, 2012.
[17] W. Yan, C. Shuang, Y. Hongnian, “Mutual information inspired feature selection using kernel canonical correlation analysis,” in Expert Systems with Applications: X, 4, 100014, 2019.
[18] D. Lin, V. D. Calhoun, Y. Wang, “Correspondence between fMRI and SNP data by group sparse canonical correlation analysis,” in Medical Image Analysis, 18(6), pp. 891-902, 2014.
[19] W. Xingjie, Z. Ling-Li, S. Hui, L. Ming, H. Yun-an, H. Dewen, “Blind source separation of functional MRI scans of the human brain based on canonical correlation analysis,” in Neurocomputing, 269, pp. 220-225, 2017.
[20] A. S. Janani, T. S. Grummett, T. W. Lewis, S. P. Fitzgibbon, E. M. Whitham, D. DelosAngeles, H. Bakhshayesh, J. O. Willoughby, K. J. Pope, “Improved artefact removal from EEG using Canonical Correlation Analysis and spectral slope,” in Journal of Neuroscience Methods, 298, pp. 1-15, 2018.
[21] M. G. Naylor, X. Lin, S. T. Weiss, B. A. Raby, C. Lange, “Using canonical correlation analysis to discover genetic regulatory variants,” in PLoS ONE, 5(5), e10395, 2010.
[22] Y. Zhang, J. Zhang, Z. Liu, Y. Liu, S. Tuo, “A network-based approach to identify disease-associated gene modules through integrating DNA methylation and gene expression,” in Biochemical and Biophysical Research Communications, 465(3), pp. 437-442, 2015.
[23] L. Liu, Q. Wang, E. Adeli, L. Zhang, H. Zhang, D. Shen, “Feature selection based on iterative canonical correlation analysis for automatic diagnosis of Parkinson’s disease,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 9901, pp. 1-8, 2016.
[24] W. Hu, D. Lin, S. Cao, J. Liu, J. Chen, V.D. Calhoun, Y. Wang, “Adaptive sparse multiple canonical correlation analysis with application to imaging (epi)genomics study of schizophrenia,” in IEEE Transactions on Biomedical Engineering, 65(2), pp. 390-399, 2019.