Abstract
Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.
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Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36:480–513
Akkoyunlu A, Altun H, Cigizoglu HK (2011) Depth-integrated estimation of dissolved oxygen in a lake. J Environ Eng 137(10):961–967
Alilou VK, Yaghmaee F (2015) Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recogn Lett 62:24–31
Antanasijević DZ, Pocajt VV, Povrenović DS, Perić-Grujić AA, Ristić MD (2013a) Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environ Sci Pollut Res 20(12):9006–9013
Antanasijević DZ, Pocajt VV, Povrenović DS, Ristić MD, Perić-Grujić AA (2013b) PM10 emission forecasting using artificial neural networks and genetic algorithm input variables optimization. Sci Total Environ 443:511–519
Antanasijević DZ, Ristić MD, Perić-Grujić AA, Pocajt VV (2013c) Forecasting human exposure to PM10 at the national level using an artificial neural network approach. J Chemom 27(6):170–177
Antanasijević DZ, Pocajt VV, Povrenović DS, Perić-Grujić AA, Ristić MD (2014a) Modelling of dissolved oxygen content Danube River using artificial neural networks and Monte Carlo simulation uncertainty analysis. J Hydrol 519:1895–1907
Antanasijević DZ, Ristić MD, Perić-Grujić AA, Pocajt VV (2014b) Forecasting GHG emissions using an optimized artificial neural network model based on correlations and principal component analysis. Int J Greenh Gas Con 20(5):244–253
Awchi TA (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour Manag 28(3):801–814
Basant N, Gupta S, Malik A, Singh KP (2010) Linear and non-linear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water—a case study. Chemometr Intell Lab 104(2):172–180
Borah DK, Bera M (2004) Watershed-scale hydrologic and nonpoint-source pollution models: reviews of application. T ASAE 47(3):789–803
Borin A, Ferräo MF, Mello C, Maretto DA, Poppi RJ (2006) Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Anal Chim Acta 579(1):25–32
Bowes MJ, Neal C, Jarvie HP, Smith JT, Davies HN (2010) Predicting phosphorus concentrations in British rivers resulting from the introduction of improved phosphorus removal from sewage effluent. Sci Total Environ 408:4239–4250
Chapra S, Pellettier G (2003) QUAL2K: a modeling framework for simulating river and stream water quality. Civil and environmental engineering dept., Tufts University, Medford
Chau KW (2006) A review on integration of artificial intelligence into water quality modeling. Mar Pollut Bull 52:726–733
Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinf 12(4):458–473
Chen WB, Liu WC (2014) Artificial neural network modeling of dissolved oxygen in reservoir. Environ Monit Assess 186(2):1203–1217
Chen JB, Li FY, Fan ZP, Wang YJ (2016) Integrated application of multivariate statistical methods to source apportionment of watercourses in the Liao River Basin, Northeast China. Int J Environ Res Public Health 13(10):1035–1061
Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68
Cox BA (2003) A review of currently available in-stream-water-quality models and their applicability for simulating dissolved oxygen in lowland rivers. Sci Total Environ 314–316:335–377
Cristianine N, Taylor JS (2000) An introduction to support vector machine and other kernel based learning methods. Cambridge University Press, Cambridge
Dibike YB, Velickov S, Solomatine DP, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15:208–216
Dogan E, Sengorur B, Koklu R (2009) Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J Environ Manag 90:1229–1235
Durand A, Devos O, Ruckebusch C, Huvenne JP (2007) Genetic algorithm optimization combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles. Anal Chim Acta 595(1–2):72–79
Fletcher D, Goss E (1993) Forecasting with neural networks: an application using bankruptcy data. Inform Manage-Amster 24:159–167
Gao C, Zhang TL (2010) Eutrophication in a Chinese context: understanding various physical and socio-economic aspects. Ambio 39:385–393
Gupta DA (2008) Implication of environmental flows in river basin management. Phys Chem Earth 33(5):298–303
Hagan MT, Demuth HP, Beale M (1996) Neural network design. PWS Publishing, Boston, MA
Haykin S (1999) Neural networks: a comprehensive foundation. Macmillan, New York
He ZB, Wen XH, 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
Heddam S (2014a) Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA. Environ Technol 35(13):1650–1657
Heddam S (2014b) Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp OR USA. Environ Sci Pollut Res 21(15):9212–9227
Hsu CW, Chang CC, Lin CJ (2007) A practical guide to support vector classification. URL<http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf>
Keerthi SS, Lin CJ (2001) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689
Khan SM, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for non-linear evaporation and evapotranspiration modeling. J Hydrol 351(3–4):299–317
Kisi O, Ozkan C, Akay B (2012) Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428–429:94–103
Kuo JT, Hsieh MH, Lung WS, She N (2007) Using artificial neural network for reservoir eutrophication prediction. Ecol Model 200(1–2):171–177
Langeron Y, Doussot M, Hewson DJ, Duchêne J (2007) Classifying NIR spectra of textile products with kernel methods. Eng Appl of Artif Intel 20(3):415–427
Legates DR, McCabe GJ Jr (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241
Lin JY, Cheng TC, Chau KW (2006) Using support vector machines for long term discharge prediction. Hydrolog Sci J 51(4):599–612
Lin SW, Lee ZJ, Chen SC, Tseng TY (2008) Parameter determination of support vectormachines and feature selection using simulated annealing approach. Appl Soft Comput 8(4):1505–1512
Mei K, Zhu YL, Liao LL, Dahlgren RA, Shang X, Zhang MH (2011) Optimizing water quality monitoring networks using continuous longitudinal monitoring data: a case study of Wen-Rui Tang River, Wenzhou, China. J Environ Monit 13:2755–2762
Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ghani AA, Chan NW (2015) Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22:6208–6219
Morse NB, Wollheim WM (2014) Climate variability masks the impacts of land use change on nutrient export in a suburbanizing watershed. Biogeochemistry 121(1):45–59
Muttil N, Chau KW (2007) Machine-learning paradigms for selecting ecologically significant input variables. Eng Appl Artif Intell 20(6):735–744
Najah A, El-Shafie A, Karim OA, El-Shafie AH (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ Sci Pollut Res 21(13):1658–1670
Nemati S, Fazelifard MH, Terzi Ö, Ghorbani MA (2015) Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong. Environ Earth Sci 74(5):1–9
Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Ghafari Gousheh M (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
Noori R, Yeh HD, Abbasi M, Kachoosangi FT, Moazami S (2015) Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand. J Hydrol 527(6):833–843
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. Chemometr Intell Lab Syst 92(2):169–178
Pierna JAF, Baeten V, Renier AM, Cogdill RP, Dardenne P (2004) Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds. J Chemom 18(7–8):341–349
Qu J, Zuo MJ (2010) Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement 43(6):781–791
Quinn NTW, Jacobs K, Chen KW, Stringfellow WT (2005) Elements of decision support system for real-time management of dissolved oxygen in the San Joaquin River deep water ship channel. Environ Model Softw 20(12):1495–1504
Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386
Ranković V, Radulović J, Radojević I, Ostojić A, Čomić L (2010) Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia. Ecol Model 221(8):1239–1244
Šiljić A, Antanasijević D, Perić-Grujić A, Ristić M, Pocajt V (2015) Artificial neural network modeling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations. Environ Sci Pollut Res 22(6):4230–4241
Singh KP, Basant A, Malik A, Jain G (2009) Artifical neural network modeling of the river water quality—a case study. Ecol Model 220(6):888–895
Singh KP, Basant N, Gupta S (2011) Support vector machines in water quality management. Anal Chim Acta 703(2):152–162
Singh KP, Gupta S, Rai P (2014) Predicting dissolved oxygen concentration using kernel regression modeling approaches with non-linear hydro-chemical data. Environ Monit Assess 186:2749–2765
State Environment Protection Bureau of China (2002a) Environmental quality standards for surface water. China Environmental Science Press, Beijing (in Chinese)
State Environment Protection Bureau of China (2002b) Water and wastewater analysis method. China Environmental Science Press, Beijing (in Chinese)
Tetratech, Inc (2002) Draft user’s manual for environmental fluid dynamics code Hydro Version (EFDC-Hydro). U.S. Environmental Protection Agency, Atlanta, GA
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Wang J, Du HY, Liu HX, Yao XJ, Hu ZD, Fan BT (2007) Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. Talanta 73(1):147–156
Wen XH, Fang J, Diao MN, Zhang XQ (2013) Artificial neural networks modeling of dissolved oxygen in the Heihe River, Northwestern China. Environ Monit Assess 185(5):4361–4371
Wool TA, Ambrose RB, Martin JL, Comer EA (2006) Water quality analysis simulation program (WASP) version 6.0 draft: user’s manual. US Environmental Protection Agency, Athens, GA.
Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93
Xie JX, Cheng CT, Chau KW, Pei YZ (2006) A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. Int J Environ Pollut 28(3–4):364–381
Yang L, Mei K, Liu X, Wu L, Zhang M, Xu J, Wang F (2013) Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China. Environ Sci Pollut Res 20(8):1–12
Yang Y, Liu XS, Li WL, Jin Y, Wu YJ, Zheng JY, Zhang WT, Chen Y (2017) Rapid measurement of epimedin A, epimedin B, epimedin C, icariin, and moisture in Herba Epimedii using near infrared spectroscopy. Spectrochim Acta Part A Mol Biomol Spectrosc 171:351–360
Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, EI-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614
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
Acknowledgements
This work was supported by the Science and Technology Department of Zhejiang Province (2008C03009). The authors would like to thank the Wenzhou Environmental Protection Bureau (WEPB) for the data provided for the Wen-Rui Tang River.
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This study was funded by the Science and Technology Department of Zhejiang Province (grant number 2008C03009).
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Ji, X., Shang, X., Dahlgren, R.A. et al. Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environ Sci Pollut Res 24, 16062–16076 (2017). https://doi.org/10.1007/s11356-017-9243-7
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DOI: https://doi.org/10.1007/s11356-017-9243-7