Modeling of Dissolved Oxygen Concentration and Its Hysteresis Behavior in Rivers Using Wavelet Transform‐Based Hybrid Models

Concentration of dissolved oxygen (DO) is an indicator to evaluate environmental health in many riverine systems. In this study two hybrid models, the wavelet-based regression model (WR) and the wavelet-based artificial neural network model (WANN), are proposed for short-interval (one day ahead) and long-interval (31 days ahead) prediction of DO concentration in Clackamas River near Oregon City, OR, USA. It was found that both in the short- and long-prediction intervals, the hybrid models performed better than the multiple linear regression and artificial neural network models. Also, the performance of the models to predict seasonal DO variations, low (DO   14 mg/L) DO ranges and simulating its hysteresis was evaluated. For the short-interval modeling, results of the WR model were better than those of the other three models. For the long-interval modeling during spring and summer, the WANN model presented better predictions of DO. And during autumn and winter, the WR model presented higher accuracy than the others. In addition, these hybrid models were able to simulate DO daily hysteresis behavior. The most accurate WR model predicted DO with the minimum root mean square error (RMSE) of 0.097 mg/L and the maximum Nash-Sutcliffe coefficient of efficiency (E) of 0.996 for the short interval. For the long interval, the best performing model was the WANN model with RMSE and E-statistics of 0.744 mg/L and 0.766, respectively.

[1]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[2]  Ahmed El-Shafie,et al.  Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations , 2011 .

[3]  Taher Rajaee,et al.  Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. , 2011, The Science of the total environment.

[4]  Xinguang He,et al.  A wavelet‐based multiple linear regression model for forecasting monthly rainfall , 2014 .

[5]  Iván Machón-González,et al.  Comparing feed-forward versus neural gas as estimators: application to coke wastewater treatment , 2013, Environmental technology.

[6]  Özgür Kişi,et al.  Evapotranspiration modeling using a wavelet regression model , 2010, Irrigation Science.

[7]  John C. Davis.  Minimal Dissolved Oxygen Requirements of Aquatic Life with Emphasis on Canadian Species: a Review , 1975 .

[8]  Vahid Nourani,et al.  Capability of Artificial Neural Network for Detecting Hysteresis Phenomenon Involved in Hydrological Processes , 2014 .

[9]  R. Romanowicz,et al.  Statistical Modeling of Algae Concentrations in the Elbe River in the Years 1985—2001 Using Observations of Daily Dissolved Oxygen, Temperature, and pH , 2003 .

[10]  A. L. Lund,et al.  砂浜海岸の生息場所でOlivella minuta(腹足類,マクラガイ)やHastula cimerea(腹足類,タケノコガイ)をTBTの指標に利用した監視 , 2015 .

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

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

[13]  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.

[14]  Somnath Mukherjee,et al.  Artificial neural network modelling in biological removal of organic carbon and nitrogen for the treatment of slaughterhouse wastewater in a batch reactor , 2014, Environmental technology.

[15]  Jiazhong Qian,et al.  Predicting river dissolved oxygen in complex watershed by using sectioned variable dimension fractal method and fractal interpolation , 2012, Environmental Earth Sciences.

[16]  Murat Küçük,et al.  Wavelet Regression Technique for Streamflow Prediction , 2006 .

[17]  Y. Demir,et al.  Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks , 2007 .

[18]  J. Wayland Eheart,et al.  Evaluation of Neural Networks for Modeling Nitrate Concentrations in Rivers , 2003 .

[19]  Mustafa Ergil,et al.  Prediction of dissolved oxygen in River Calder by noise elimination time series using wavelet transform , 2016, J. Exp. Theor. Artif. Intell..

[20]  Kwok-Wing Chau,et al.  A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States , 2015, Environmental Monitoring and Assessment.

[21]  K. Mervyn Curtis,et al.  Neural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns , 2003, Comput. Electr. Eng..

[22]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[23]  Mohammad H. Aminfar,et al.  A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation , 2009, Eng. Appl. Artif. Intell..

[24]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

[25]  Ahmed El-Shafie,et al.  Application of artificial neural networks for water quality prediction , 2012, Neural Computing and Applications.

[26]  Ozgur Kisi,et al.  River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model , 2011 .

[27]  Dünyamin Güçlü,et al.  Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN) , 2008 .

[28]  Qing-Song Xu,et al.  Support vector machines and its applications in chemistry , 2009 .

[29]  A. G. Frenich,et al.  Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality. , 2001, Water research.

[30]  Fatih Evrendilek,et al.  Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks , 2014, Environmental Monitoring and Assessment.

[31]  Asaad Y. Shamseldin,et al.  Comparative study of different wavelet based neural network models for rainfall–runoff modeling , 2014 .

[32]  Hong Zhang,et al.  Analyzing sediment dissolved oxygen based on microprofile modeling , 2014, Environmental Science and Pollution Research.

[33]  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.

[34]  Taher Rajaee,et al.  Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model , 2015, Environmental Monitoring and Assessment.

[35]  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.

[36]  Mohammad Hosein Yas,et al.  Application of imperialist competitive algorithm and neural networks to optimise the volume fraction of three-parameter functionally graded beams , 2014, J. Exp. Theor. Artif. Intell..

[37]  Miklas Scholz,et al.  Modeling of Dissolved Oxygen Applying Stepwise Regression and a Template-Based Fuzzy Logic System , 2014 .

[38]  G. Sekaran,et al.  Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network. , 2007, Journal of environmental sciences.

[39]  Yi Wang,et al.  Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China , 2013, Environmental Science and Pollution Research.

[40]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

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

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

[43]  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.

[44]  Taher Rajaee,et al.  Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models , 2015 .

[45]  Salim Heddam,et al.  Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study , 2013, Environmental Monitoring and Assessment.

[46]  Taher Rajaee,et al.  Evaluation of wavelet-GEP and wavelet-ANN hybrid models for prediction of total nitrogen concentration in coastal marine waters , 2016, Arabian Journal of Geosciences.

[47]  O. Kisi.  Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting , 2011 .

[48]  Vinit Sehgal,et al.  Wavelet regression models for predicting flood stages in rivers: a case study in Eastern India , 2013 .

[49]  Mohammad Zounemat-Kermani,et al.  Hydrometeorological Parameters in Prediction of Soil Temperature by Means of Artificial Neural Network: Case Study in Wyoming , 2013 .