Abstract
Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health’s maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The potential of the BMA was investigated and compared with five data-driven methods, extreme leaning machine (ELM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), classification and regression tree (CART), and multilinear regression (MLR), by considering hourly temperature, pH, and specific conductivity data as inputs. The methods were compared with respect to three statistics, root mean square errors (RMSE), Nash-Sutcliffe efficiency, and determination coefficient. Results based on two stations’ data indicated that the proposed method performed superior to the ELM, ANN, ANFIS, CART, and MLR in estimation of hourly dissolved oxygen; corresponding improvements obtained by BMA are about 5–8%, 13–12%, 7–9%, and 18–27% with respect to RMSE. The ELM also outperformed the other four methods (ANN, ANFIS, CART, and MLR), and the CART and MLR indicated the lowest estimation accuracy in both stations. Examination of various input combinations revealed that the most effective variable is water temperature while the specific conductivity has negligible effect on hourly dissolved oxygen.
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References
Ahmed AAM (2017) Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). J King Saud Univ - Eng Sci 29(2):151–158
Akkoyunlu A, Altun H, Cigizoglu HK (2011) Depth-integrated estimation of dissolved oxygen in a lake. J Environ Eng 137(10):961–967
Alizamir M, Kisi O, Zounemat-Kermani M (2018) Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrol Sci J 63(1):63–73
Antanasijevic’ D, Pocajt V, Peric’-Grujic’ A, Ristic’ M (2019) Multilevel split of high-dimensional water quality data using artificial neural networks for the prediction of dissolved oxygen in the Danube River. Neural Comput Applic. https://doi.org/10.1007/s00521-019-04079-y
Antanasijevic’ D, Pocajt V, Peric’-Grujic’ A, Ristic’ M (2013) Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo simulation uncertainty analysis. J Hydrol 519:1895–1907
Ay M, Kisi O (2012) Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado. J Environ Eng 138(6):654–662
Ay M, Kisi O (2017) Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques. KSCE J Civ Eng 21(5):1631–1639
Barzegar R, Asghari Moghaddam A, Adamowski J, Ozga-Zielinski B (2018) Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stoch Env Res Risk A 32(3):799–813
Breiman L, Friedman J, Olshen R (1984) Classification and regression trees. Wadsworth Belement, California
Brodnjak-Vonina D, Dobnik D, Novi M, Zupan J (2002) Chemometrics characterisation of the quality of river water. Anal Chim Acta 462(1):87–100
Bueno-Crespo A, García-Laencina PJ, Sancho-Gómez J-L (2013) Neural architecture design based on extreme learning machine. Neural Netw 48:19–24
Csábrági A et al (2019) Estimation of dissolved oxygen in riverine ecosystems: comparison of differently optimized neural networks. Ecol Eng 138:298–309
Deo RC, Downs N, Parisi A, Adamowski J, Quilty J (2017) Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle. Environ Res 155:141–166
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(2):1229–1235
Duan Q, Ajami NK, Gao X, Sorooshian S (2007) Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 30:1371–1386. https://doi.org/10.1016/j.advwatres.2006.11.014
Duan Q, Phillips TJ (2010) Bayesian estimation of local signal and noise in multi-model simulations of climate change. J Geophys Res Atmos 115:1–15. https://doi.org/10.1029/2009JD013654
Elkiran G, Nourani V, Abba SI (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577:123962
Fijani E, Barzegar R, Deo R, Tziritis E, Konstantinos S (2018) Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Sci Total Environ 648:839–853
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993
Hagan MT, Delmuth H, Beale M (1996) Neural network design. PWS Publishing Company, Boston
Hao Y, Baikb J, Choi M (2019) Combining generalized complementary relationship models with the Bayesian model averaging method to estimate actual evapotranspiration over China. Agric For Meteorol 279(2019):107759
He J, Chua A, Ryanb MC, Valeoa C, Zaitlin B (2011) Abiotic influences on dissolved oxygen in a riverine environment. Ecol Eng 37:1804–1814
Heddam S (2017) Fuzzy neural network (EFuNN) for modelling dissolved oxygen concentration (DO). Intell Syst Environ Manag: Theory and Applications, Intelligent Systems Reference Library 113:231–253. https://doi.org/10.1007/978-3-319-42993-9_11
Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. DOI 24:16702–16724. https://doi.org/10.1007/s11356-017-9283-z
Heddam S, Kisi O (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol. https://doi.org/10.1016/j.jhydrol.2018.02.061
Hoang T-HT, Nguyen VD, Van AD, Nguyen HTT (2019) Decision tree techniques to assess the role of daily DO variation in classifying shallow eutrophicated lakes in Hanoi, Vietnam. Water Qual Res J. https://doi.org/10.2166/wqrj.2019.105
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. IEEE Int Conf Neural Netw Conf Proc 2:985–990
Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122
Huang J, Gao J (2017) An ensemble simulation approach for artificial neural network: an example from chlorophyll a simulation in Lake Poyang, China. Ecol Inform 37:52–58
Jang S (1993) Adaptive network-based fuzzy inference system. IEEE Journal 23(3):665–685
Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing: a computing approach to learning and machine intelligence. Prentice Hall, Englewood Cliffs
Keshtegar B, Heddam S (2017) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Applic. https://doi.org/10.1007/s00521-017-2917-8
Keshtegar B, Heddam S, Hosseinabadi H (2019) The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river. Environ Earth Sci 78:34–18. https://doi.org/10.1007/s12665-018-8028-8
Kisi O, Alizamir M (2018) Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: wavelet extreme learning machine vs wavelet neural networks. Agric For Meteorol 263:41–48
Li Y, Andersen HE, McGaughey R (2008) A comparison of statistical methods for estimating forest biomass from light detection and ranging data. West J Appl For 23:223–231
Liu S, Yan M, Tai H, Xu L, Li D (2012). Prediction of dissolved oxygen content in aquaculture of Hyriopsis cumingii using Elman neural network. In: Li D, Chen Y (eds) Computer and computing technologies in agriculture V. CCTA 2011. IFIP advances in information and communication technology 370. Springer, Berlin
Marquardt D (1963) An algorithm for least-squares estimations of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441
Martí P, Shiri J, Duran-Ros M, Arbat G, de Cartagena FR, Puig-Bargués J (2013) Artificial neural networks vs. gene expression programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput Electron Agric 99:176–185
Moazamnia M, Hassanzadeh Y, Nadiri AA, Khatibi R, Sadeghfam S (2019) Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.02.011
Morellos A et al (2016) Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosyst Eng doi.org/10.1016/j.biosystemseng.2016.04.018
Noori R, Safavi S, Shahrokni SAN (2013) A reduced-order adaptive neurofuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand. J Hydrol 495:175–185
Olyaie E, Abyaneh HZ, Mehr AD (2017) A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. Geosci Front 8:517–527
Qi CS, Yu ZH, Hou Z (2010) Study on quality prediction of the complex production based on CART algorithm. Modul Mach Tool Automatic Manuf Tech 3:94–97
Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133:1155–1174
Raheli B, Aalami MT, Ahmed El-Shafie A, Ghorbani MA, Deo C, R. (2017) Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 76:503. https://doi.org/10.1007/s12665-017-6842-z
Ranković V, Radulović J et al (2010) Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia. Ecol Model 221(8):1239–1244
Sengorur B, Dogan E, Koklu R, Samandar A (2006) Dissolved oxygen estimation using artificial neural network for water quality control. Fresenius Environ Bull 15(9a):1064–1067
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(2):188–192
Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality - a case study. Ecol Model 220(6):888–895
Tao H, Bobaker AM, Ramal MM, Yaseen ZM, Hossain MS, Shahid S (2018) Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models. Environ Sci Pollut Res 26:923–937. https://doi.org/10.1007/s11356-018-3663-x
Vrugt JA, Robinson BA (2007) Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging. Water Resour Res 43:W01411
Xu J, Anctil F, Boucher MA (2019) Hydrological post-processing of streamflow forecasts issued from multi-model ensemble prediction systems. J Hydrol 578:124002
Yadav B, Ch S, Mathur S, Adamowski J (2017) Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. J Water Land Dev 32(1):103–112
Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125
Zhang HJ, Zhang PX, Chen JH (2005) On-line quality inspection of spot welding based on classification and regression tree (CART). J Lanzhou Univ Technol 31(4):10–14
Zhu S, Heddam S (2019) Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN). Water Qual Res J. https://doi.org/10.2166/wqrj.2019.053
Zounemat-Kermani M, Seo Y, Kim S, Ghorbani MA, Samadianfard S, Naghshara S, Kim NW, Singh VP (2019) Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Appl Sci 9:2534. https://doi.org/10.3390/app9122534
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Kisi, O., Alizamir, M. & Docheshmeh Gorgij, A. Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res 27, 9589–9603 (2020). https://doi.org/10.1007/s11356-019-07574-w
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DOI: https://doi.org/10.1007/s11356-019-07574-w