Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

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 (R2), 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, R2, 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.

[1]  Fayun Li,et al.  Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China , 2016, International journal of environmental research and public health.

[2]  W. Wollheim,et al.  Climate variability masks the impacts of land use change on nutrient export in a suburbanizing watershed , 2014, Biogeochemistry.

[3]  Salim Heddam,et al.  Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA , 2014, Environmental technology.

[4]  H. K. Cigizoglu,et al.  Depth-Integrated Estimation of Dissolved Oxygen in a Lake , 2011 .

[5]  Yong Pan,et al.  Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds , 2008 .

[6]  C. Ruckebusch,et al.  Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles. , 2007, Analytica chimica acta.

[7]  Viktor Pocajt,et al.  Forecasting human exposure to PM10 at the national level using an artificial neural network approach , 2013 .

[8]  Davor Antanasijević,et al.  Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations , 2015, Environmental Science and Pollution Research.

[9]  David J. Hewson,et al.  Classifying NIR spectra of textile products with kernel methods , 2007, Eng. Appl. Artif. Intell..

[10]  Jan-Tai Kuo,et al.  USING ARTIFICIAL NEURAL NETWORK FOR RESERVOIR EUTROPHICATION PREDICTION , 2007 .

[11]  Zne-Jung Lee,et al.  Parameter determination of support vector machine and feature selection using simulated annealing approach , 2008, Appl. Soft Comput..

[12]  Davor Antanasijević,et al.  Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study , 2013, Environmental Science and Pollution Research.

[13]  Dawei Han,et al.  Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction , 2011 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Martin T. Hagan,et al.  Neural network design , 1995 .

[16]  Chun Kiat Chang,et al.  Prediction of water quality index in constructed wetlands using support vector machine , 2015, Environmental Science and Pollution Research.

[17]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[18]  Hung Soo Kim,et al.  Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling , 2008 .

[19]  Nitin Muttil,et al.  Machine-learning paradigms for selecting ecologically significant input variables , 2007, Eng. Appl. Artif. Intell..

[20]  Minghua Zhang,et al.  Optimizing water quality monitoring networks using continuous longitudinal monitoring data: a case study of Wen-Rui Tang River, Wenzhou, China. , 2011, Journal of environmental monitoring : JEM.

[21]  Steven C. Chapra,et al.  QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality , 2004 .

[22]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[23]  Wen-Cheng Liu,et al.  Artificial neural network modeling of dissolved oxygen in reservoir , 2014, Environmental Monitoring and Assessment.

[24]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[25]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[26]  Zhide Hu,et al.  Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. , 2007, Talanta.

[27]  Ahmed El-Shafie,et al.  Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring , 2014, Environmental Science and Pollution Research.

[28]  Kwok-wing Chau,et al.  A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity , 2006 .

[29]  Davor Z Antanasijević,et al.  PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization. , 2013, The Science of the total environment.

[30]  C. Neal,et al.  Predicting phosphorus concentrations in British rivers resulting from the introduction of improved phosphorus removal from sewage effluent. , 2010, The Science of the total environment.

[31]  E. Doğan,et al.  Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. , 2009, Journal of environmental management.

[32]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[33]  Maryam Abbasi,et al.  Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand , 2015 .

[34]  James J. Fitzpatrick,et al.  Water Quality Analysis Simulation Program (WASP) , 1900 .

[35]  Mohammad Ali Ghorbani,et al.  Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong , 2015, Environmental Earth Sciences.

[36]  Jasna Radulović,et al.  Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia , 2010 .

[37]  Premanjali Rai,et al.  Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data , 2014, Environmental Monitoring and Assessment.

[38]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[39]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[40]  R. Deo,et al.  Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq , 2016 .

[41]  K. Chau,et al.  A hybrid model coupled with singular spectrum analysis for daily rainfall prediction , 2010 .

[42]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[43]  K. Hanna.  Use and implications of iron and other transition metals in environmental remediation processes , 2012, Environmental Science and Pollution Research.

[44]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[45]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[46]  R. Poppi,et al.  Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. , 2006, Analytica chimica acta.

[47]  Viktor Pocajt,et al.  Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis , 2014 .

[48]  Nikita Basant,et al.  Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water — A case study , 2010 .

[49]  Chuanqi Zhang,et al.  Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China , 2013, Environmental Monitoring and Assessment.

[50]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[51]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[52]  X. Wen,et al.  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 , 2014 .

[53]  Deva K. Borah,et al.  WATERSHED-SCALE HYDROLOGIC AND NONPOINT-SOURCE POLLUTION MODELS: REVIEW OF APPLICATIONS , 2004 .

[54]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[55]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[56]  C. L. Wu,et al.  Methods to improve neural network performance in daily flows prediction , 2009 .

[57]  Ming J. Zuo,et al.  Support vector machine based data processing algorithm for wear degree classification of slurry pump systems , 2010 .

[58]  A. Gupta.  Implication of environmental flows in river basin management , 2008 .

[59]  Farzin Yaghmaee,et al.  Application of GRNN neural network in non-texture image inpainting and restoration , 2015, Pattern Recognit. Lett..

[60]  Taolin Zhang,et al.  Eutrophication in a Chinese Context: Understanding Various Physical and Socio-Economic Aspects , 2010, AMBIO.

[61]  Ozgur Kisi,et al.  Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm , 2012 .

[62]  Viktor Pocajt,et al.  Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis , 2014 .

[63]  B A Cox,et al.  A review of currently available in-stream water-quality models and their applicability for simulating dissolved oxygen in lowland rivers. , 2003, The Science of the total environment.

[64]  Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China , 2013, Environmental Science and Pollution Research.

[65]  Zoran Stevanović,et al.  Introductory editorial thematic issue: Mediterranean karst hydrogeology , 2015, Environmental Earth Sciences.

[66]  Taymoor A. Awchi,et al.  River Discharges Forecasting In Northern Iraq Using Different ANN Techniques , 2014, Water Resources Management.

[67]  Kwok-wing Chau,et al.  A review on integration of artificial intelligence into water quality modelling. , 2006, Marine pollution bulletin.

[68]  K. P. Singh,et al.  Support vector machines in water quality management. , 2011, Analytica chimica acta.

[69]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[70]  Yongjiang Wu,et al.  Rapid measurement of epimedin A, epimedin B, epimedin C, icariin, and moisture in Herba Epimedii using near infrared spectroscopy. , 2017, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[71]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

[72]  Salim Heddam,et al.  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 , 2014, Environmental Science and Pollution Research.

[73]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.