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Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study

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Abstract

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.

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References

  • Basant, N., Gupta, S., Malik, A., & Singh, K. P. (2010). Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water—a case study. Chemometrics and Intelligent Laboratory Systems, 104, 172–180. doi:10.1016/j.chemolab.2010.08.005.

    Article  CAS  Google Scholar 

  • Benoit, P., Gratton, Y., & Mucci, A. (2006). Modeling of dissolved oxygen levels in the bottom waters of the Lower St. Lawrence Estuary: coupling of benthic and pelagic processes. Marine Chemistry, 102, 13–32. doi:10.1016/j.marchem.2005.09.015.

    Article  CAS  Google Scholar 

  • Boano, F., Revelli, R., & Ridolfi, L. (2006). Stochastic modelling of DO and BOD components in a stream with random inputs. Advances in Water Resources, 29, 1341–1350. doi:10.1016/j.advwatres.2005.10.007.

    Article  Google Scholar 

  • Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52, 726–733. doi:10.1016/j.marpolbul.2006.04.003.

    Article  CAS  Google Scholar 

  • Chen, D., Lu, J., & Shen, Y. (2010). Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non-point source polluted river in Zhejiang Province, southeast China. Hydrologie Continentale, 24, 290–299. doi:10.1002/hyp.7482.

    Google Scholar 

  • Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2, 267–278.

    Google Scholar 

  • Chiu, S. (1996). Method and software for extracting fuzzy classification rules by subtractive clustering. In: Fuzzy Information Proceeding Society, Biennial Conference of the North American, pp. 461–465.

  • Diamantopoulou, M. J., Antonopoulos, V. Z., & Papamichail, D. M. (2007). Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers. Water Resour Manage, 21, 649–662. doi:10.1007/s11269-006-9036-0.

    Article  Google Scholar 

  • Eftekhari, M., & Katebi, S. D. (2008). Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter. Applied Mathematical Modelling, 32, 2634–2651. doi:10.1016/j.apm.2007.09.023.

    Article  Google Scholar 

  • Facchini, A., Mocenni, C., Marwan, N., Vicino, A., & Tiezzi, E. (2007). Nonlinear time series analysis of dissolved oxygen in the Orbetello Lagoon (Italy). Ecological Modelling, 203, 339–348. doi:10.1016/j.ecolmodel.2006.12.001.

    Article  Google Scholar 

  • Giusti, E., & Marsili-Libelli, S. (2009). Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition. Ecological Modelling, 220, 2415–2426. doi:10.1016/j.ecolmodel.2009.06.007.

    Article  CAS  Google Scholar 

  • Heddam, S., Bermad, A., & Dechemi, N. (2012). ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environmental Monitoring and Assessment, 184, 1953–1971. doi:10.1007/s10661-011-2091-x.

    Article  CAS  Google Scholar 

  • Hull, V., Parrella, L., & Falcucci, M. (2008). Modelling dissolved oxygen dynamics in coastal lagoons. Ecological Modelling, 211, 468–480. doi:10.1016/j.ecolmodel.2007.09.023.

    Article  Google Scholar 

  • Ishibuchi H., Nakashima T., Murata T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems Man and Cybernetics—Part B: Cybernetics, Vol. 29, NO. 5, October. Doi: 10.1109/3477.790443.

  • Jamieson, T. S., Schiff, S. L., & Taylor, W. D. (2013). Using stable isotopes of dissolved oxygen for the determination of gas exchange in the Grand River, Ontario, Canada. Water Research, 47, 781–790. doi:10.1016/j.watres.2012.11.001.

    Article  CAS  Google Scholar 

  • Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.

    Article  Google Scholar 

  • Jang, J. S. R., & Gulley, N. (1996). Fuzzy logic toolbox: reference manual. Natick: The MathWorks Inc.

    Google Scholar 

  • Kayombo, S., Mbwette, T. S. A., Mayo, A. W., Katima, J. H. Y., & Jorgensen, S. E. (2000). Modelling diurnal variation of dissolved oxygen in waste stabilization ponds. Ecological Modelling, 127, 21–31.

    Article  CAS  Google Scholar 

  • Li-Hua, C., & Li, L. (2008). Evaluation of dissolved oxygen in water by artificial neural network and sample optimization. Journal of Central South University of Technology, 15(s2), 416–420. doi:10.1007/s11771-008-0498-5.

    Article  Google Scholar 

  • 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. D. Li and Y. Chen (Eds.): computer and computing technologies in agriculture V (CCTA) 2011, Part III. IFIP Advances in Information and Communication Technology, 370, 508–518. doi:10.1007/978-3-642-27275-2-57.

    Article  Google Scholar 

  • Lohani, A. K., Goel, N. K., & Bhatia, K. K. S. (2006). Takagi-Sugeno fuzzy inference system for modeling stage–discharge relationship. Journal of Hydrology, 331, 146–160. doi:10.1016/j.jhydrol.2006.05.007.

    Article  Google Scholar 

  • Mandal, S., Debnath, M., Ray, S., Ghosh, P. B., Roy, M., & Ray, S. (2012). Dynamic modelling of dissolved oxygen in the creeks of Sagar Island, Hooghly-Matla estuarine system, West Bengal, India. Applied Mathematical Modelling, 36, 5952–5963. doi:10.1016/j.apm.2011.10.013.

    Article  Google Scholar 

  • Martin, N., McEachern, P., Yu, T., & Zhu, D. Z. (2013). Model development for prediction and mitigation of dissolved oxygen sags in the Athabasca River, Canada. Science of the Total Environment, 443, 403–412. doi:10.1016/j.scitotenv.2012.10.030.

    Article  CAS  Google Scholar 

  • Misra, A. K. (2011). Modeling the depletion of dissolved oxygen due to algal bloom in a lake by taking Holling type-III interaction. Applied Mathematics and Computation, 217, 8367–8376. doi:10.1016/j.amc.2011.03.034.

    Article  Google Scholar 

  • Misra, A. K., Chandra, P., & Raghavendra, V. (2011). Modeling the depletion of dissolved oxygen effect of time delay. Advances in Water Resources, 34, 1232–1238. doi:10.1016/j.advwatres.2011.05.010.

    Article  CAS  Google Scholar 

  • Money, E., Carter, G. P., & Serre, M. L. (2009). Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey. Water Research, 43, 1948–1958. doi:10.1016/j.watres.2009.01.034.

    Article  CAS  Google Scholar 

  • Nakova, E., Linnebank, F. E., Bredeweg, B., Salles, P., & Uzunov, Y. (2009). The river MESTA case study: a qualitative model of dissolved oxygen in aquatic ecosystems. Ecological Informatics, 4, 339–357. doi:10.1016/j.ecoinf.2009.09.015.

    Article  Google Scholar 

  • Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short-term flood forecasting with a neurofuzzy model. Water Resources Research, 41, W04004. doi:10.1029/2004WR003562.

    Article  Google Scholar 

  • Pinto, U., Maheshwari, B., Shrestha, S., & Morris, C. (2012). Modelling eutrophication and microbial risks in peri-urban river systems using discriminant function analysis. Water Research, 46, 6476–6488. doi:10.1016/j.watres.2012.09.025.

    Article  CAS  Google Scholar 

  • Radwan, M., Willems, P., El–Sadek, A., & Berlamont, J. (2003). Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model. International Journal of River Basin Management, 1(2), 97–103. doi:10.1080/15715124.2003.9635196.

    Article  Google Scholar 

  • Ranković, V., Radulović, J., Radojević, I., Ostojić, A., & Ćomić, L. (2010). Neural network modeling of dissolved oxygen in the Gruźa reservoir, Serbia. Ecological Modelling, 221, 1239–1244. doi:10.1016/j.ecolmodel.2009.12.023.

    Article  Google Scholar 

  • Rounds, S.A., Sullivan, A.B.. (2009). Review of Klamath River total maximum daily load models from Link River Dam to Keno Dam, Oregon: U.S. Geological Survey Administrative Report, 37P. http://www.usbr.gov/mp/kbao/waterquality/klamath_river_model_review_final.pdf.

  • Singaraja, C., Chidambaram, S., Prasanna, M. V., Paramaguru, P., Johnsonbabu, G., Thivya, C., et al. (2011). A study on the behavior of the dissolved oxygen in the shallow coastal wells of Cuddalore District, Tamilnadu, India. Water Quality Exposure Health. doi:10.1007/s12403-011-0058-3.

    Google Scholar 

  • Stefan, H. G., & Fang, X. (1994). Dissolved oxygen model for regional lake analysis. Ecological Modelling, 71, 37–68.

    Article  CAS  Google Scholar 

  • Sullivan, A.B.., Rounds, S.A., Deas, M.L., and Sogutlugil, I.E. (2012). Dissolved oxygen analysis, TMDL model comparison, and particulate matter shunting—preliminary results from three model scenarios for the Klamath River upstream of Keno Dam, Oregon: U.S. Geological Survey Open-File Report 2012–1101, 30 p.

  • USGS (2013). http://ga.water.usgs.gov/edu/dissolvedoxygen.html.

  • Wei, M., Bai, B., Sung, A. H., Liu, Q., Wang, J., & Cather, M. E. (2007). Predicting injection profiles using ANFIS. Information Sciences, 177, 4445–4461. doi:10.1016/j.ins.2007.03.021.

    Article  Google Scholar 

  • Williams, R. J., & Boorman, D. B. (2012). Modelling in-stream temperature and dissolved oxygen at sub-daily time steps: an application to the River Kennet, UK. Science of the Total Environment, 423, 104–110. doi:10.1016/j.scitotenv.2012.01.054.

    Article  CAS  Google Scholar 

  • Yager, R. R., & Filev, D. P. (1994). Approximate clustering via the mountain method. IEEE Transactions on Systems, Man, and Cybernetics, 24(8), 1279–1284.

    Article  Google Scholar 

  • Yu, W., & Li, X. (2009). Online fuzzy modeling with structure and parameter learning. Expert Systems with Applications, 36, 7484–7492. doi:10.1016/j.eswa.2008.09.016.

    Article  Google Scholar 

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Acknowledgments

The author thanks the staffs of USGS web server for providing the data that makes this research possible.

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  1. Faculty of Science, Agronomy Department, Hydraulics Division University 20 Août 1955, Route EL HADAIK, BP 26, Skikda, Algeria

    Salim Heddam

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Heddam, S. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environ Monit Assess 186, 597–619 (2014). https://doi.org/10.1007/s10661-013-3402-1

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