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A decision fusion method based on classification models for water quality monitoring

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Abstract

Monitoring of water quality is one of the world’s main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009–2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

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Data availability

All data generated or analyzed during this study are included in this published article; they are available from the corresponding author on reasonable request. For the purposes of privacy, all used data are confidential and cannot be made available.

References

  • Abbasi T, Abbasi SA (2012) Water quality indices. 1st Edition, Elsevier, Hardback ISBN 978-0-444-54304-2

  • Abedi M, Norouzi GH, Bahroudi A (2012) Support vector machine for multi-classification of mineral prospectivity areas. Comput Geosci 46:272–283

    Article  CAS  Google Scholar 

  • Adem K, Kiliçarslan S, Cömert O (2019) Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Syst Appl 115:557–564

    Article  Google Scholar 

  • Areerachakul S, Sanguansintukul S (2010) Classification and regression trees and MLP neural network to classify water quality of canals in Bangkok, Thailand. Int J Intell Comput Res 1(2):30–37

    Google Scholar 

  • Ayeni O (2013) Interpretation of surface water quality using principal components analysis and cluster analysis. J Geogr Reg Plan 6(4):132–141

    Article  Google Scholar 

  • Bae MH, Wu T, Pan R (2010) Mix-ratio sampling : classifying multiclass imbalanced mouse brain images using support vector machine. Expert Syst Appl 37(7):4955–4965

    Article  Google Scholar 

  • Bhardwaj V, Singh DS, Singh AK (2010) Water quality of the Chhoti Gandak River using principal component analysis, Ganga Plain. India J Earth Syst Sci 119(1):117–127

    Article  CAS  Google Scholar 

  • Bigdeli B, Samadzadegan F, Reinartz P (2015) Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system. Int J Appl Earth Obs Geoinf 38:309–320

    Google Scholar 

  • Bouamar M, Ladjal M (2012) Performance evaluation of three pattern classification techniques used for water quality monitoring. Int J Comput Intell 11(02):1250013

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  • Cao L, Chua K, Chong W, Lee H, Gu Q (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1–2):321–336

    Google Scholar 

  • Chen K, Chen H, Zhou C, Huang Y, Qi X, Shen R, Liu F, Zuo M, Zou X, Wang J, Zhang Y, Chen D, Chen X, Deng Y, Ren H (2020) Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Res 171:115454

    Article  CAS  Google Scholar 

  • Chen W, Zhang SW, Cheng YM, Pan Q (2010) Prediction of protein–protein interaction types using the decision templates based on multiple classier fusion. Math Comput Model 52(11–12):2075–2084

    Article  Google Scholar 

  • Chou JS, Ho CC, Hoang HS (2018) Determining quality of water in reservoir using machine learning. Eco Inform 44:57–75

    Article  Google Scholar 

  • Gakii C, Jepkoech J (2019) Classification model for water quality analysis using decision tree. Eur J Comput Sci Inf Technol 7(3):1–8 (June 2019)

    Google Scholar 

  • Deng S, Lin SY, Chang WL (2011) Application of multiclass support vector machines for fault diagnosis of field air defense gun. Expert Syst Appl 38(5):6007–6013

    Article  Google Scholar 

  • Décret exécutif N° 11–125 du 22 Mars (2011) Relatif à la qualité de l’eau de consommation humaine, Journal officiel de la Republique Algerienne N° 18

  • De León HRH (2006) Supervision et diagnostic des procédés de production d'eau potable (Doctoral dissertation, INSA de Toulouse)

  • Dilmi S, Ladjal M (2021) A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques. Chemom Intell Lab Syst 214:104329

    Article  CAS  Google Scholar 

  • Djerioui M, Bouamar M, Ladjal M, Zerguine A (2018) Chlorine soft sensor based on extreme learning machine for water quality monitoring. Arab J Sci Eng 44(3):2033–2044

    Article  Google Scholar 

  • Hamlat A, Guidoum A, Koulala I (2016) Status and trends of water quality in the Tafna catchment : a comparative study using water quality indices. J Water Reuse Desalination 7(2):228–245

    Article  Google Scholar 

  • Haghiabi AH, Nasrolahi AH, Parsaie A (2018) Water quality prediction using machine learning methods. Water Qual Res J 53(1):3–13

    Article  CAS  Google Scholar 

  • Haghighi MS, Vahedian A, Yazdi HS (2011) Extended decision template presentation for combining classifiers. Expert Syst Appl 38(7):8414–8418

    Article  Google Scholar 

  • Hend S, Al-Khalifa A, Al-Ajlan A (2010) Automatic readability measurements of the arabic text: an exploratory study. Arab J Sci Eng 35(2C):103–124

    Google Scholar 

  • Horng MH (2009) Multi-class support vector machine for classification of the ultrasonic images of supraspinatus. Expert Syst Appl 36(4):8124–8133

    Article  Google Scholar 

  • Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306

    Google Scholar 

  • Jiang Y, Li C, Sun L, Guo D, Zhang Y, Wang W (2021) A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks. J Clean Prod 318:128533

    Article  CAS  Google Scholar 

  • Jin JL, Liu L, Ding J, Fu Q (2003) Logistic curve model of groundwater quality evaluation. Environ Pollut Cont 25(1):46–48

    Google Scholar 

  • Jolliffe IT (2002) Principal component analysis, Springer Series in Statistics, 2nd edn. Springer

  • Kumar R, Jayaraman V, Kulkarni B (2005) An SVM classifier incorporating simultaneous noise reduction and feature selection : illustrative case examples. Pattern Recogn 38(1):41–49

    Article  Google Scholar 

  • Kuncheva LI, Bezdek JC, Duin RP (2001) Decision templates for multiple classifier fusion : an experimental comparison. Pattern Recogn 34(2):299–314

    Article  Google Scholar 

  • Kuncheva LI (2014) Combining pattern classifiers: methods and algorithms. John Wiley & Sons

  • Ladjal M, Ouali MA, Lass MD (2020) optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring. In 2020 International Conference on Electrical Engineering (ICEE). IEEE, pp 1–6

  • Ladjal M, Bouamar M, Djerioui M, Brik Y (2016) Performance evaluation of ANN and SVM multiclass models for intelligent water quality classification using Dempster-Shafer Theory. In: 2016 International Conference on Electrical and Information Technologies (ICEIT). IEEE, pp 191–196

  • Liao Y, Xu J, Wang W (2011) A method of water quality assessment based on biomonitoring and multiclass support vector machine. Procedia Environ Sci 10:451–457

    Article  CAS  Google Scholar 

  • Liu D, Zou Z (2012) Water quality evaluation based on improved fuzzy matter-element method. J Environ Sci 24(7):1210–1216

    Article  CAS  Google Scholar 

  • Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58(3–4):458–465

    Article  Google Scholar 

  • Min JK, Cho SB (2007) Multiple classifier fusion using k-nearest localized templates. In: International Conference on Intelligent Data Engineering and Automated Learning. Springer, Berlin, Heidelberg, pp 447–456

  • Modaresi F, Araghinejad S (2014) A comparative assessment of support vector machines, probabilistic neural networks, and k-nearest neighbor algorithms for water quality classification. Water Resour Manage 28(12):4095–4111

    Article  Google Scholar 

  • Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ghani AA, Chan NW (2014) Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22(8):6208–6219

    Article  Google Scholar 

  • Msiza IS, Nelwamondo FV, Marwala T (2008) Water demand prediction using artificial neural networks and support vector regression. J Comput 3(11):1–8

    Article  Google Scholar 

  • Muharemi F, Logofătu D, Andersson C, Leon F (2018) Approaches to building a detection model for water quality: a case study. In Modern approaches for intelligent information and database systems. Springer, Cham, pp 173–183

  • Nieto PG, Fernández JA, Suárez VG, Muñiz CD, García-Gonzalo E, Bayón RM (2015) A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir : a case study in Northern Spain. Appl Math Comput 260:170–187

    Google Scholar 

  • Ocampo-Duque W, Ferré-Huguet N, Domingo JL, Marta S (2006) Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int 32(6):733–742

    Article  CAS  Google Scholar 

  • Oukil A, Soltani AA, Boutaghane H, Abdalla O, Bermad A, Hasbaia M, Boulassel MR (2021) A surrogate water quality index to assess groundwater using a unified DEA-OWA framework. Environ Sci Pollut Res 28(40):56658–56685

    Article  Google Scholar 

  • Phadatare SS, Gawande S (2016) Review paper on development of water quality index. International Journal of Engineering Research and Technology (IJERT) 5(5):765–767

    Google Scholar 

  • Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21–45

    Article  Google Scholar 

  • Rachedi LH, Amarchi H (2015) Assessment of the water quality of the Seybouse River (north-east Algeria) using the CCME WQI model. Water Supply 15(4):793–801

    Article  CAS  Google Scholar 

  • Ruta D, Gabrys B (2005) Classifier selection for majority voting. Information Fusion 6(1):63–81

    Article  Google Scholar 

  • Saint-Jean C, Frélicot C (2001) An hybrid parametric model for semi-supervised robust clustering. In: Int. Conf. on Recent Developments in Mixture Modelling (MIXTURES)

  • Schölkopf B, Smola AJ, Bach F (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press

  • Semmlow JL (2004) Biosignal and medical image processing (Signal Processing and Communications, 22). CRC Press

    Google Scholar 

  • Singh KP, Basant N, Gupta S (2011) Support vector machines in water quality management. Anal Chim Acta 703(2):152–162

    Article  CAS  Google Scholar 

  • Soltani AA, Oukil A, Boutaghane H, Bermad A, Boulassel MR (2021) A new methodology for assessing water quality, based on data envelopment analysis : application to Algerian dams. Ecol Ind 121:106952

    Article  CAS  Google Scholar 

  • Soltani AA, Bermad A, Boutaghane H, Oukil A, Abdalla O, Hasbaia M, Oulebsir R, Zeroual S, Lefkir A (2020) An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria). Environ Monit Assess 192(10):1–17

    Article  Google Scholar 

  • Übeyli ED (2009) Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines. Expert Syst 26(3):249–259

    Article  Google Scholar 

  • Vapnik V (2000) The nature of statistical learning theory. Springer-Verlag, New York

    Book  Google Scholar 

  • Wang LJ, Zou ZH (2008) Application of improved attributes recognition method in water quality assessment. Chinese J Environ Eng 2(4):553–556

    CAS  Google Scholar 

  • Wang ZY, Yang YF (2010) Multi-class cluster support vector machines. J Comput Appl 30(1):143–145

    CAS  Google Scholar 

  • Wang Y, Wang P, Bai Y, Tian Z, Li J, Shao X, Mustavich LF, Li BL (2013) Assessment of surface water quality via multivariate statistical techniques : a case study of the Songhua River Harbin region. China J Hydro-Environ Res 7(1):30–40

    Article  Google Scholar 

  • Wang Q, Li S, Li R (2019) Evaluating water resource sustainability in Beijing, China : combining PSR model and matter-element extension method. J Clean Prod 206:171–179

    Article  Google Scholar 

  • Widodo A, Yang BS (2007) Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst Appl 33(1):241–250

    Article  Google Scholar 

  • Wu CH, Tzeng GH, Goo YJ, Fang WC (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397–408

    Article  Google Scholar 

  • Yan H, Zou Z, Wang H (2010) Adaptive neuro fuzzy inference system for classification of water quality status. J Environ Sci 22(12):1891–1896

    Article  Google Scholar 

  • Yang BS, Han T, Yin ZJ (2006) Fault diagnosis system of induction motors using feature extraction, feature selection and classification algorithm. JSME Int J, Ser C 49(3):734–741

    Article  Google Scholar 

  • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138

    Article  Google Scholar 

  • Zhang SW, Liu YF, Yu Y, Zhang TH, Fan XN (2014) MSLoc-DT : a new method for predicting the protein subcellular location of multispecies based on decision templates. Anal Biochem 449:164–171

    Article  CAS  Google Scholar 

  • Zhang W, Gao H, Sun H (2018) Application and analysis of Bayesian method and grey relational analysis in marine water quality evaluation. IOP Conf Ser Earth Environ Sci 182:012007

    Article  Google Scholar 

  • Zhou W, Wu B (2008) Assessment of soil erosion and sediment delivery ratio using remote sensing and GIS : a case study of upstream Chaobaihe River catchment, north China. Int J Sedim Res 23(2):167–173

    Article  Google Scholar 

  • Zou ZH, Yun Y, Sun JN (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci 18(5):1020–1023

    Article  CAS  Google Scholar 

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Acknowledgements

This work is supported by the General Directorate of Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research of Algeria. The authors thank the editor and reviewer for many helpful and constructive suggestions and remarks about an earlier draft of this article which improved the paper quality considerably. The authors would like to thank the engineers from the Tilesdit dam direction for their support and for providing the facilities for this investigation and free access to databases and valuable guidance for the field sampling.

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Authors and Affiliations

  1. LASS, Laboratory of Analysis of Signals and Systems, Department of Electronics, Faculty of Technology, University of M’sila, M’sila, Algeria

    Mohamed Ladjal, Mohamed Bouamar, Youcef Brik & Mohamed Djerioui

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  1. Mohamed Ladjal
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  2. Mohamed Bouamar
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  3. Youcef Brik
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  4. Mohamed Djerioui
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Contributions

All the authors contributed to the study conception and design through material preparation, data collection, and analysis. All the authors read and approved the final manuscript.

Mohamed Ladjal: conceptualization, methodology, software, formal analysis, investigation, resources, data curation and collection, writing — original draft, writing — review and editing, visualization.

Mohamed Bouamar: supervision, project administration, conceptualization, formal analysis, writing — review and editing, visualization.

Youcef Brik: conceptualization, software, formal analysis, investigation, visualization.

Mohamed Djerioui: software, formal analysis, investigation.

Corresponding author

Correspondence to Mohamed Ladjal.

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Responsible Editor: Xianliang Yi

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Highlights

• New intelligent water quality classification is performed using data combination fusion and features selection.

• ANN and SVM methods have been proposed for water quality classification status.

• The final decision is performed using decision templates rule combination based on probabilistic output from both the two classifiers.

• Real database from Tilesdit dam (Algeria) are used for evaluation.

• A superior accuracy of up to 99.24% was obtained by the proposed approach and of all used methods.

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Ladjal, M., Bouamar, M., Brik, Y. et al. A decision fusion method based on classification models for water quality monitoring. Environ Sci Pollut Res 30, 22532–22549 (2023). https://doi.org/10.1007/s11356-022-23418-6

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