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Prediction of water quality index in constructed wetlands using support vector machine

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Abstract

Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R 2) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations.

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References

  • Bateni SM, Borghei SM, Jeng DS (2007) Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Eng Appl Artif Intell 20:401–414

    Article  Google Scholar 

  • Betbeder J, Rapinel S, Corpetti T, Pottier E, Corgne S & Hubert-Moy L (2013) Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping. Proc SPIE Int Soc Opt Eng

  • Brix H (1997) Do macrophytes play a role in constructed treatment wetlands? Water Sci Technol 35:11–17

    Article  CAS  Google Scholar 

  • Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27

    Article  Google Scholar 

  • Chen S-T, Yu P-S (2007) Real-time probabilistic forecasting of flood stages. J Hydrol 340:63–77

    Article  Google Scholar 

  • Dadaser-Celik F, Cengiz E (2013) A neural network model for simulation of water levels at the Sultan Marshes wetland in Turkey. Wetl Ecol Manag 21:297–306

    Article  Google Scholar 

  • Department of Environment. Malaysia Environmental Quality Report (2005) Department of Environment, Ministry of Natural Resources and Environment, Petaling Jaya, Malaysia

  • Diamantopoulou MJ, Antonopoulos VZ, Papamichail DM (2005) The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece. J Oper Res, Springer-Verlag, 115–125

  • Dong Y, Scholz M, Harrington R (2012) Statistical modeling of contaminants removal in mature integrated constructed wetland sediments. J Environ Eng (United States) 138:1009–1017

    Article  CAS  Google Scholar 

  • Dronova I, Gong P, Clinton NE, Wang L, Fu W, Qi S, Liu Y (2012) Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sens Environ 127:357–369

    Article  Google Scholar 

  • Espejo L, Kretschmer N, Oyarzún J, Meza F, Núñez J, Maturana H, Soto G, Oyarzo P, Garrido M, Suckel F, Amezaga J, Oyarzún R (2012) Application of water quality indices and analysis of the surface water quality monitoring network in semiarid North-Central Chile. Environ Monit Assess 184:5571–5588

    Article  Google Scholar 

  • Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64:2409–2420

    Article  CAS  Google Scholar 

  • Ghani AA, Azamathulla HM (2014) Development of GEP-based functional relationship for sediment transport in tropical rivers. Neural Comput & Applic 24:271–276

    Article  Google Scholar 

  • Guardo M (1999) Hydrologic balance for a subtropical treatment wetland constructed for nutrient removal. Ecol Eng 12:315–337

    Article  Google Scholar 

  • Ha H, Stenstrom MK (2003) Identification of land use with water quality data in stormwater using a neural network. Water Res 37:4222–4230

    Article  CAS  Google Scholar 

  • He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386

    Article  Google Scholar 

  • Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. <http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf>

  • Juahir H, Zain SM, Toriman ME, Mokhtar M, Man HC (2004) Application of artificial neural network models for predicting water quality index. J KejuruteraanAwam 16:42–55

    Google Scholar 

  • Kadlec RH, Wallace SD (2008) Treatment wetlands, 2nd edn. CRC, Boca Raton

    Book  Google Scholar 

  • Kakaei Lafdani E, Moghaddam Nia A, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62

    Article  Google Scholar 

  • Khalil B, Ouarda TBMJ, St-Hilaire A (2011) Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydrol 405:277–287

    Article  CAS  Google Scholar 

  • Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11:199–205

    Article  Google Scholar 

  • Khuan LY, Hamzah N, Jailani R (2002) Prediction of water quality index (WQI) based on artificial neural network (ANN). In: Proceedings of the student conference on research and development, Shah Alam, Malaysia

  • LiW, Cui L, Zhang Y, Zhang M, Zhao X & Wang Y (2013) Statistical modeling of phosphorus removal in horizontal subsurface constructed wetland. Wetlands, 1–11

  • Lin S-W, Lee Z-J, Chen S-C, Tseng T-Y (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8:1505–1512

    Article  Google Scholar 

  • Mitsch WJ, Gosselink JG (2007) Wetlands, 4th edn. Wiley, New York

    Google Scholar 

  • Mohammadpour R, Ghani AA, Azamathulla HM (2013a) Prediction of equilibrium scours time around long abutments. Proc Inst Civ Eng Water Manag 166(7):394–401

    Article  Google Scholar 

  • Mohammadpour R, Ab. Ghani A & Azamathulla HM (2011) Estimating time to equilibrium scour at long abutment by using genetic programming. 3rd International conference on managing rivers in the 21st century, rivers 2011, 6th–9th December, Penang, Malaysia

  • Mohammadpour R, Ghani AA, Azamathulla HM (2013b) Estimation of dimension and time variation of local scour at short abutment. Int J Rivers Basin Manag 11:121–135

    Article  Google Scholar 

  • Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ghani AA (2014) Spatial pattern analysis for water quality in free surface constructed wetland. Water Sci and Technol 70:1161–1167

  • Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Gousheh MG (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401:177–189

    Article  Google Scholar 

  • Nourani V, Rezapour Khanghah T & Sayyadi M (2013) Application of the artificial neural network to monitor the quality of treated water. Int J Manag Inf Technol, 3

  • Pan Y, Jiang J, Wang R, Cao H (2008) Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds. Chemom Intell Lab Syst 92:169–178

    Article  CAS  Google Scholar 

  • Qu J, Zuo MJ (2010) Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement 43:781–791

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (2012) Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran. Ecol Model 244:117–126

    Article  Google Scholar 

  • Schmid BH, Koskiaho J (2006) Artificial neural network modeling of dissolved oxygen in a wetland pond: the case of Hovi, Finland. J Hydrol Eng 11:188–192

    Article  Google Scholar 

  • Shaharuddin S, Zakaria NA, Ghani AA, Chang CK (2013) Performance evaluation of constructed wetland in Malaysia for water security enhancement. Proceedings of 2013 IAHR World Congress, China

    Google Scholar 

  • Shih SS, Kuo PH, Fang WT, Lepage BA (2013) A correction coefficient for pollutant removal in free water surface wetlands using first-order modeling. Ecol Eng 61:200–206

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Singh G, Kandasamy J, Shon HK, Chob J (2011b) Measuring treatment effectiveness of urban wetland using hybrid water quality—artificial neural network (ANN) model. Desalin Water Treat 32:284–290

    Article  CAS  Google Scholar 

  • Sivapragasam C, Muttil N (2005) Discharge rating curve extension—a new approach. Water Resour Manag 19:505–520

    Article  Google Scholar 

  • Smola A, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    Article  Google Scholar 

  • Song K, Park YS, Zheng F, Kang H (2013) The application of artificial neural network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands. Ecol Inform 16:10–16

    Article  Google Scholar 

  • Tabari H, Kisi O, Ezani A, Hosseinzadeh Talaee P (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444–445:78–89

    Article  Google Scholar 

  • Tomenko V, Ahmed S, Popov V (2007) Modelling constructed wetland treatment system performance. Ecol Model 205:355–364

    Article  Google Scholar 

  • Ustun B, Melssen WJ, Oudenhuijzen M, Buydens LMC (2005) Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Anal Chim Acta 544:292–305

    Article  Google Scholar 

  • Vanlandeghem MM, Meyer MD, Cox SB, Sharma B, Patiño R (2012) Spatial and temporal patterns of surface water quality and ichthyotoxicity in urban and rural river basins in Texas. Water Res 46:6638–6651

    Article  CAS  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Vymazal J (2011) Enhancing ecosystem services on the landscape with created, constructed and restored wetlands. Ecol Eng 37:1–5

    Article  Google Scholar 

  • Wang J, Du H, Liu H, Yao X, Hu Z, Fan B (2007) Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. Talanta 73:147–156

    Article  CAS  Google Scholar 

  • Wang L, Li X, Cui W (2012) Fuzzy neural networks enhanced evaluation of wetland surface water quality. Int J Comput Appl Technol 44:235–240

    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 33:241–250

    Article  Google Scholar 

  • Xie X, Liu WT, Tang B (2008) Spacebased estimation of moisture transport in marine atmosphere using support vector regression. Remote Sens Environ 112:1846–1855

    Article  Google Scholar 

  • Zakaria NA, Ghani AA, Abdullah R, Mohd Sidek L, Ainan A (2003) Bio-ecological drainage system (BIOECODS) for water quantity and quality control. Int J Rivers Basin Manag 1:237–251

    Article  Google Scholar 

  • Zedler JB, Kercher S (2005) Wetland resources: status, trends, ecosystem services, and restorability. Annu Rev Environ Resour 30:39–74

    Article  Google Scholar 

  • Zhang C & Xie Z (2013) Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands, 1–12

  • Zhang T, Xu D, He F, Zhang Y, Wu Z (2012) Application of constructed wetland for water pollution control in China during 1990–2010. Ecol Eng 47:189–197

    Article  Google Scholar 

  • Zhang H, Sun L, Sun T, Li H, Luo Q (2013a) Spatial distribution and seasonal variation of polycyclic aromatic hydrocarbons (PAHs) contaminations in surface water from the Hun River, Northeast China. Environ Monit Assess 185:1451–1462

    Article  CAS  Google Scholar 

  • Zhang Y, Cui L, Li W, Zhang M, Zhao X, Wang Y (2013b) Modeling phosphorus removal in horizontal subsurface constructed wetland based on principal component analysis. Nongye Gongcheng Xuebao Trans Chin Soc Agric Eng 29:200–207

    Google Scholar 

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Acknowledgments

The authors would like to acknowledge the financial assistance from the Ministry of Education under the Long Term Research Grant (LRGS) No. 203/PKT/672004 entitled “Urban Water Cycle Processes, Management and Societal Interactions: Crossing from Crisis to Sustainability.” This study is funded under a subproject entitled “Sustainable Wetland Design Protocol for Water Quality Improvement” (Grant number: 203/PKT/6724002).

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Authors and Affiliations

  1. River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300, Nibong Tebal, Penang, Malaysia

    Reza Mohammadpour, Syafiq Shaharuddin, Chun Kiat Chang, Nor Azazi Zakaria, Aminuddin Ab Ghani & Ngai Weng Chan

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  1. Reza Mohammadpour
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  2. Syafiq Shaharuddin
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  3. Chun Kiat Chang
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  4. Nor Azazi Zakaria
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  5. Aminuddin Ab Ghani
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  6. Ngai Weng Chan
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Correspondence to Reza Mohammadpour.

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Mohammadpour, R., Shaharuddin, S., Chang, C.K. et al. Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22, 6208–6219 (2015). https://doi.org/10.1007/s11356-014-3806-7

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  • DOI: https://doi.org/10.1007/s11356-014-3806-7

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