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Year 2021, , 441 - 451, 30.06.2021

Sayiter Yıldız , Can Bülent Karakuş

https://doi.org/10.17776/csj.897185

Abstract

References

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  • [2] Chow C.W.K., Van Leeuwen J.A., Drikas M., Fabris R., Spark K.M., Page D.W., The impact of the character of natural organic matter in conventional treatment with alum, Water Science and Tech., 40(9) (1999) 97–104.
  • [3] Bridgeman J., Bieroza M., Baker A., The application of fluorescence spectroscopy to organic matter characterisation in drinking water treatment, Reviews in Environmental Science and Bio/Tech., 10(3) (2011) 277–290.
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  • [5] Sillanpää M., Ncibi M.C. Matilainen A., Vepsäläinen M., Removal of natural organic matter in drinking water treatment by coagulation: a comprehensive review, Chemosphere, 190 (2018) 54-71.
  • [6] Fallahizadeh S., Neamati B., Fadaei A., Mengelizadeh N., Removal of Natural Organic Matter (NOM), Turbidity, and Color of surface water by integration of enhanced coagulation process and direct filtration, Journal of Advances in Environmental Health Res., 5(2) (2017) 108-113.
  • [7] Gümüş D., Akbal F., Removal of Natural Organıc Matter In Drınkıng Waters And Preventıon Of Trıhalomethanes Formatıon, Sigma: Journal of Engineering & Natural Sci., 31(4) (2013).
  • [8] Trinh T.K., Kang L.S., Response surface methodological approach to optimize the coagulation-flocculation process in drinking water treatment, Chem. Eng. Res. Des., 89(7) (2011) 1126-1135.
  • [9] Ramos H. M., Loureiro D., Lopes A., Fernandes C., Covas D., Reis L.F., Cunha M.C., Evaluation of chlorine decay in drinking water systems for different flow conditions: from theory to practice, Water Resources Managment, 24(4) (2010) 815-834.
  • [10] WHO, Guidelines for drinking water quality: training pack, 4nd ed. Geneva Switzerland, (2017) 564.
  • [11] Mann A.G., Tam C.C., Higgins C.D., Rodrigues L.C., The association between drinking water turbidity and gastrointestinal illness: a systematic review, BMC Public Health., 7(1) (2007) 256.
  • [12] Rouse R., New Drinking Water Regulations in the UK. London , Drinking Water Inspectorate, (2001).
  • [13] Whitfield P., Goals and data collection designs for water quality monitoring, Water Resour. Bull., 24 (4) (1988) 775–780.
  • [14] Khalil B., Ouarda T.B.M.J., Statistical approaches used to assess and redesign surface water quality monitoring networks, J. Environ. Monit., 11 (2009) 1915–1929.
  • [15] Chen J.C., Chang N.B. Shieh W.K., Assessing wastewater reclamation potential by neural network model, Eng. Appl. Artif. Intell., 16 (2003) 149–157.
  • [16] Li R.Z., Advanced and trend analysis of theoretical methodology for water quality forecast, J. Hefei Univ. Technol., 29 (2006) 26–30.
  • [17] Xiang S.L., Liu Z.M., Ma L.P., Study of multivariate linear regression analysis model for ground water quality prediction, Guizhou Sci., 24 (2006) 60–62.
  • [18] Niu Z.G., Zhang H.W., Liu H.B., Application of neural network to prediction of coastal water quality, J. Tianjin Polytechnic Univ., 25 (2006) 89–92.
  • [19] Shu J., Using neural network model to predict water quality, North Environ., 31 (2006) 44–46.
  • [20] Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S., Application of neural networks to modelling nonlinear relationships in ecology, Ecol. Model., 90 (1996) 39–52.
  • [21] Chu W.C., Bose N.K., Speech signal prediction using feedforward neural network, Electro. Lett., 34 (1998) 999–1001.
  • [22] Messikh N., Samar M.H., Messikh L., Neural network analysis of liquid–liquid extraction of phenol from wastewater using TBP solvent, Desalination., 208 (2007) 42–48.
  • [23] Hanbay D., Turkoglu I., Demir Y., Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks, Expert Syst. Appl., 34 (2008) 1038–1043.
  • [24] Yıldız S., Değirmenci M., Estimation of oxygen exchange during treatment sludge composting through multiple regression and artificial neural networks, International J. of Environmental Res., 9(4), (2015) 1173–1182.
  • [25] Yildiz, S., Artificial neural network (ANN) approach for modeling Zn (II) adsorption in batch process, Korean J. of Chemical Eng., 34(9) (2017) 2423-2434.
  • [26] Yıldız S., Artificial neural network (ANN) approach For modeling of Ni(II) adsorption from aqueous solution by peanut shell, Ecol. Chem. Eng. S., 25(4) (2018) 581-604.
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  • [30] Shu C., Ouarda T.B.M.J., Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space, Water Resour. Res., 43 (2007).
  • [31] Rezvan K., Fakhri Y., Mehrorang G., Kheibar D., Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study, Chemometrics and Intelligent Laboratory Systems., 159 (2016) 127–137.
  • [32] Nabavi-Pelesaraei A., Kouchaki-Penchah H., Amid S., Modeling and optimization of CO2 emissions for tangerine production using artificial neural networks and data envelopment analysis, International Journal of Biosci., 4(7) (2014) 148–158.
  • [33] Singh K.P., Basant A., Malik A., Jain G., Artificial neural network modeling of the river water quality-a case study, Ecological Model., 220(6) (2009) 888-895.
  • [34] Alves E.M., Rodrigues R.J., Dos Santos Corrêa C., Fidemann T., Rocha J.C., Buzzo J.L.L., et al., Use of ultraviolet–visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index, Env. Monit. and Assess., 190(6) (2018) 319.
  • [35] Olyaie E., Banejad H., Chau K.W., Melesse A.M., A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States, Env. Monit. and Assess., 187(4) (2015) 189.
  • [36] Yıldız S., Karakuş C.B., Estimation of irrigation water quality index with development of an optimum model: a case study, Environment, Development and Sustain., 22 (2020) 4771–4786.
  • [37] Esri., ArcGIS için Desktop spatial analiz, Esri Bilgi Sistemleri Mühendislik ve Eğitim Ltd. Şti. 1. Baskı, Ankara, (2014).
  • [38] Loyd C.D., Local Models for Spatial Analysis, 2nd ed. ISBN 9780367864934, Temple University, Philadelphia, PA, USA., (2010) 98.
  • [39] Aksu H.H., Hepdeniz K., Mapping with the aid of Geographic Information System and analysis of annual and monthly average maximum air temperature distribution in Burdur. Mehmet Akif Ersoy University Journal of the Graduate School of Natural and Applied Sci., 7 (2016) 202-214.

Estimation of organic matter dependent on different variables in drinking water network using artificial neural network and multiple regression methods

Year 2021, , 441 - 451, 30.06.2021

Sayiter Yıldız , Can Bülent Karakuş

https://doi.org/10.17776/csj.897185

Abstract

The aim of this study is to estimate of organic matter values based on chlorine and turbidity values with the help of ANN and multiple regression (MR) methods. Three different models were done with ANN, and the statistical performance of these models was evaluated with statistical parameters like; µ, SE, σ, R2, RMSE and MAPE. The R2 value of the selected best model was found to be quite high with 0.94. The relationship between the evaluation results of the ANN model and the empirical data (R2 = 0.92) showed that the model was quite successful. In the MR analysis, R2 was determined as 0.63, and a middling significant (p <0.05) relationship was found. Since the calculated F value was greater than the tabulated F value, it was concluded that there is a clear relationship between dependent and independent variables. In addition, spatial distribution maps of chlorine, turbidity, organic matter values were created with the help of the GIS. With these maps, the estimated distribution of the measured parameters in the whole city network was accomplished. This study revealed that turbidity and chlorine parameters are related to organic matter value, and by establishing this relationship, organic matter can be estimated by ANN.

Keywords

Drinking water network organic matter Artificial neural network Multiple regression methods

References

  • [1] Khalil B., Ouarda T.B.M.J., St-Hilaire A., Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis, Journal of Hydrology., 405 (2011) 277–287.
  • [2] Chow C.W.K., Van Leeuwen J.A., Drikas M., Fabris R., Spark K.M., Page D.W., The impact of the character of natural organic matter in conventional treatment with alum, Water Science and Tech., 40(9) (1999) 97–104.
  • [3] Bridgeman J., Bieroza M., Baker A., The application of fluorescence spectroscopy to organic matter characterisation in drinking water treatment, Reviews in Environmental Science and Bio/Tech., 10(3) (2011) 277–290.
  • [4] Matilainen A., Removal of the natural organic matter in the different stage of the drinking water treatment process, Thesis for the degree of PD, University of technology, (2007).
  • [5] Sillanpää M., Ncibi M.C. Matilainen A., Vepsäläinen M., Removal of natural organic matter in drinking water treatment by coagulation: a comprehensive review, Chemosphere, 190 (2018) 54-71.
  • [6] Fallahizadeh S., Neamati B., Fadaei A., Mengelizadeh N., Removal of Natural Organic Matter (NOM), Turbidity, and Color of surface water by integration of enhanced coagulation process and direct filtration, Journal of Advances in Environmental Health Res., 5(2) (2017) 108-113.
  • [7] Gümüş D., Akbal F., Removal of Natural Organıc Matter In Drınkıng Waters And Preventıon Of Trıhalomethanes Formatıon, Sigma: Journal of Engineering & Natural Sci., 31(4) (2013).
  • [8] Trinh T.K., Kang L.S., Response surface methodological approach to optimize the coagulation-flocculation process in drinking water treatment, Chem. Eng. Res. Des., 89(7) (2011) 1126-1135.
  • [9] Ramos H. M., Loureiro D., Lopes A., Fernandes C., Covas D., Reis L.F., Cunha M.C., Evaluation of chlorine decay in drinking water systems for different flow conditions: from theory to practice, Water Resources Managment, 24(4) (2010) 815-834.
  • [10] WHO, Guidelines for drinking water quality: training pack, 4nd ed. Geneva Switzerland, (2017) 564.
  • [11] Mann A.G., Tam C.C., Higgins C.D., Rodrigues L.C., The association between drinking water turbidity and gastrointestinal illness: a systematic review, BMC Public Health., 7(1) (2007) 256.
  • [12] Rouse R., New Drinking Water Regulations in the UK. London , Drinking Water Inspectorate, (2001).
  • [13] Whitfield P., Goals and data collection designs for water quality monitoring, Water Resour. Bull., 24 (4) (1988) 775–780.
  • [14] Khalil B., Ouarda T.B.M.J., Statistical approaches used to assess and redesign surface water quality monitoring networks, J. Environ. Monit., 11 (2009) 1915–1929.
  • [15] Chen J.C., Chang N.B. Shieh W.K., Assessing wastewater reclamation potential by neural network model, Eng. Appl. Artif. Intell., 16 (2003) 149–157.
  • [16] Li R.Z., Advanced and trend analysis of theoretical methodology for water quality forecast, J. Hefei Univ. Technol., 29 (2006) 26–30.
  • [17] Xiang S.L., Liu Z.M., Ma L.P., Study of multivariate linear regression analysis model for ground water quality prediction, Guizhou Sci., 24 (2006) 60–62.
  • [18] Niu Z.G., Zhang H.W., Liu H.B., Application of neural network to prediction of coastal water quality, J. Tianjin Polytechnic Univ., 25 (2006) 89–92.
  • [19] Shu J., Using neural network model to predict water quality, North Environ., 31 (2006) 44–46.
  • [20] Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S., Application of neural networks to modelling nonlinear relationships in ecology, Ecol. Model., 90 (1996) 39–52.
  • [21] Chu W.C., Bose N.K., Speech signal prediction using feedforward neural network, Electro. Lett., 34 (1998) 999–1001.
  • [22] Messikh N., Samar M.H., Messikh L., Neural network analysis of liquid–liquid extraction of phenol from wastewater using TBP solvent, Desalination., 208 (2007) 42–48.
  • [23] Hanbay D., Turkoglu I., Demir Y., Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks, Expert Syst. Appl., 34 (2008) 1038–1043.
  • [24] Yıldız S., Değirmenci M., Estimation of oxygen exchange during treatment sludge composting through multiple regression and artificial neural networks, International J. of Environmental Res., 9(4), (2015) 1173–1182.
  • [25] Yildiz, S., Artificial neural network (ANN) approach for modeling Zn (II) adsorption in batch process, Korean J. of Chemical Eng., 34(9) (2017) 2423-2434.
  • [26] Yıldız S., Artificial neural network (ANN) approach For modeling of Ni(II) adsorption from aqueous solution by peanut shell, Ecol. Chem. Eng. S., 25(4) (2018) 581-604.
  • [27] Demuth H., Beale M., Neural network toolbox for use with MATLAB, The MathWorks Inc. Natick, (2001) 840.
  • [28] Smith M., Neural Networks for Statistical Modelling, Van Nostrand Reinhold, NY., (1994) 235.
  • [29] Dreyfus G., Martinez J.M., Samuelides M., Gordon M.B., Badran F., Thiria S., Herault, Drinking Water and Health, Vol. 2., National Academy of Sciences, Washington, DC., (1980).
  • [30] Shu C., Ouarda T.B.M.J., Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space, Water Resour. Res., 43 (2007).
  • [31] Rezvan K., Fakhri Y., Mehrorang G., Kheibar D., Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study, Chemometrics and Intelligent Laboratory Systems., 159 (2016) 127–137.
  • [32] Nabavi-Pelesaraei A., Kouchaki-Penchah H., Amid S., Modeling and optimization of CO2 emissions for tangerine production using artificial neural networks and data envelopment analysis, International Journal of Biosci., 4(7) (2014) 148–158.
  • [33] Singh K.P., Basant A., Malik A., Jain G., Artificial neural network modeling of the river water quality-a case study, Ecological Model., 220(6) (2009) 888-895.
  • [34] Alves E.M., Rodrigues R.J., Dos Santos Corrêa C., Fidemann T., Rocha J.C., Buzzo J.L.L., et al., Use of ultraviolet–visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index, Env. Monit. and Assess., 190(6) (2018) 319.
  • [35] Olyaie E., Banejad H., Chau K.W., Melesse A.M., A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States, Env. Monit. and Assess., 187(4) (2015) 189.
  • [36] Yıldız S., Karakuş C.B., Estimation of irrigation water quality index with development of an optimum model: a case study, Environment, Development and Sustain., 22 (2020) 4771–4786.
  • [37] Esri., ArcGIS için Desktop spatial analiz, Esri Bilgi Sistemleri Mühendislik ve Eğitim Ltd. Şti. 1. Baskı, Ankara, (2014).
  • [38] Loyd C.D., Local Models for Spatial Analysis, 2nd ed. ISBN 9780367864934, Temple University, Philadelphia, PA, USA., (2010) 98.
  • [39] Aksu H.H., Hepdeniz K., Mapping with the aid of Geographic Information System and analysis of annual and monthly average maximum air temperature distribution in Burdur. Mehmet Akif Ersoy University Journal of the Graduate School of Natural and Applied Sci., 7 (2016) 202-214.
There are 39 citations in total.

Details

Primary Language English
Subjects Statistics
Journal Section Natural Sciences
Authors

Sayiter Yıldız 0000-0002-3382-2487

Can Bülent Karakuş 0000-0002-7373-9960

Publication Date June 30, 2021
Submission Date March 15, 2021
Acceptance Date June 4, 2021
Published in Issue Year 2021

Cite

APA Yıldız, S., & Karakuş, C. B. (2021). Estimation of organic matter dependent on different variables in drinking water network using artificial neural network and multiple regression methods. Cumhuriyet Science Journal, 42(2), 441-451. https://doi.org/10.17776/csj.897185