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Feasibility Assessment of Data-Driven Models in Predicting Pollution Trends of Omerli Lake, Turkey

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Abstract

Data-driven models are commonly used in a wide range of disciplines, including environmental engineering. To analyze Omerli Lake’s historic water pollution status, this study monitors data for dissolved oxygen, 5-day biochemical oxygen demand, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen, and ortho phosphate. The quality of the lake water is assessed based on measurements of dissolved oxygen. The collected data are analyzed using regression analysis and artificial neural network models. The main goal of this paper is to reveal the best applicable data-driven model in order to gain forward-looking information regarding the dissolved oxygen level of the lake using other pollution parameters. In order to ascertain eutrophic status, total phosphorus loads for each year are represented on a Vollenweider diagram. Results designate an increasing risk of eutrophication for Omerli Lake in recent years. Results of the data-driven models show that the artificial neural networks model constitutes the best relationship between the dissolved oxygen and other parameters.

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References

  • Abdul-Wahab SA, Al-Alawi SM (2008) Prediction of sulfur dioxide (SO2) concentration levels from the Mina Al-Fahal Refinery in Oman using artificial neural networks. Am J Environ Sci 4(5):473–481

    Article  Google Scholar 

  • Abo-Quadis S, Alhiary A (2007) Statistical models for traffic noise at signalized intersections. Build Environ 42(8):2939–2948

    Article  Google Scholar 

  • Akkoyunlu A (2003) Evaluation of eutrophication process in Lake Iznik. Fresenius Environ Bull 12(12):801–807

    Google Scholar 

  • Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20(7):851–871

    Article  Google Scholar 

  • Baykal BB, Tanik A, Gonenc IE (2000) Water quality in drinking water reservoirs of a megacity, Istanbul. Environ Manag 26(6):607–614

    Article  Google Scholar 

  • Chang IC, Hsiao TY (2004) Short-term model of the production of construction aggregates in Taiwan based on artificial neural networks. Environ Sci Pollut Res Int 11(2):84–90

    Article  Google Scholar 

  • Coskun HG, Alparslan E (2008) Environmental modelling of Omerli catchment area in Istanbul, Turkey using remote sensing and GIS techniques. Environ Monit Assess 153:323–332

    Article  Google Scholar 

  • Coskun HG, Tanik A, Alganci U, Cigizoglu HK (2008) Determination of environmental quality of a drinking water reservoir by remote sensing, GIS and regression analysis. Water Air Soil Pollut 194:275–285

    Article  Google Scholar 

  • Demuth H, Beale M (1998) Neural network toolbox. The MathWorks, Inc, Natick

  • Domagalski J, Lin C, Luo Y, Kang J, Wang S, Brown LR, Munnc MD (2007) Eutrophication study at the Panjiakou–Daheiting reservoir system, northern Hebei Province, People’s Republic of China: Chlorophyll-a model and sources of phosphorus and nitrogen. Agric Water Manag 94(1–3):43–53

    Google Scholar 

  • Elhatip H, Hinis MA, Gulbahar N (2008) Evaluation of the water quality at Tahtali dam watershed in Izmir-Turkey by means of statistical methodology. Stoch Environ Res Risk Assess 22(3):391–400

    Article  Google Scholar 

  • Gumrah F, Oz B, Guler B, Evin S (2000) The application of artificial neural networks for the prediction of water quality of polluted aquifer. Water Air Soil Pollut 119(1–4):275–294

    Article  Google Scholar 

  • Havens KE, Fukushima T, Xie P, Iwakuma T, James RT, Takamura N, Hanazato T, Yamamoto T (2001) Nutrient dynamics and the eutrophication of shallow lakes Kasumigaura (Japan), Donghu (PR China), and Okeechobee (USA). Environ Pollut 111(2):263–272

    Article  Google Scholar 

  • Hinck JE, Blazer VS, Denslow ND, Myers MS, Gross TS, Tillitt DE (2007) Biomarkers of contaminant exposure in Northern Pike (Esox lucius) from the Yukon Stream basin. Alaska Arch Environ Contam Toxicol 52(4):549–562

    Article  Google Scholar 

  • Hutchins MG, Dilks C, Davies HN, Deflandre A (2007) Issues of diffuse pollution model complexity arising from performance benchmarking. Hydrol Earth Syst Sci 11(1):647–662

    Article  Google Scholar 

  • ISKI (Istanbul Water and Sewerage Authority) (2008) Omerli drinking water treatment plants. Istanbul, Turkey. http://www.iski.gov.tr. Accessed Jan 2009

  • Karakoc G, Erkoc FU, Katircioglu H (2003) Water quality and impacts of pollution sources for Eymir and Mogan Lakes (Turkey). Environ Int 29(1):21–27

    Article  Google Scholar 

  • Karul C, Soyupak S, Cilesiz AF, Akbay N, Germen E (2000) Case studies on the use of neural networks in eutrophication modeling. Ecol Model 134(2):145–152

    Article  Google Scholar 

  • Koklu R, Sengorur B, Topal B (2009) Water quality assessment using multivariate statistical methods-a case study: Melen River system (Turkey). Water Resour Manag. doi:10.1007/s11269-009-9481-7

    Google Scholar 

  • Kuo YM, Liu CW, Lin KH (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38(1):148–158

    Article  Google Scholar 

  • Li R, Dong M, Zhao Y, Zhang L, Cui Q, He W (2007) Assessment of water quality and identification of pollution sources of plateau lakes in Yunnan (China). J Environ Qual 36:291–297

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Modell Softw 15(1):101–124

    Article  Google Scholar 

  • Manache G, Meiching CS (2004) Sensitivity analysis of a water-quality model using Latin hypercube sampling. J Water Resour Plan Manage 130(3):232–242

    Article  Google Scholar 

  • Markfort CD, Hondzo M (2009) Dissolved oxygen measurements in aquatic environments: the effects of changing temperature and pressure on three sensor technologies. J Environ Qual 38:1766–1774

    Article  Google Scholar 

  • Mitra B, Scott HD, Dixon JC, McKimmey JM (1998) Applications of fuzzy logic to the prediction of soil erosion in a large watershed. Geoderma 86(3–4):183–209

    Article  Google Scholar 

  • Papanastasiou DK, Melas D, Kioutsioukis I (2007) Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean City. Water Air Soil Pollut 182(1–4):325–334

    Article  Google Scholar 

  • Serengil Y, Gokbulak F, Ozhan S, Hızal A, Sengonul K, Balci AN, Ozyuvaci N (2007) Hydrological impacts of a slight thinning treatment in a deciduous forest ecosystem in Turkey. J Hydrol 333(2–4):569–577

    Article  Google Scholar 

  • Shirsath PB, Singh AK (2009) A comparative study of daily pan evaporation estimation using ANN, Regression and climate based models. Water Resour Manag. doi:10.1007/s11269-009-9514-2

    Google Scholar 

  • SPSS (2007) Neural networks, user’s guide, version 17.0. SPSS Inc

  • Suen JP, Eheart JW (2003) Evaluation of neural networks for modeling nitrate concentrations in streams. J Water Resour Plan Manage 129(6):505–510

    Article  Google Scholar 

  • Tayfur G, Singh VP (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. J Hydraul Eng ASCE 132(12):1321–1330

    Article  Google Scholar 

  • Thomas S, Jacko RB (2007) Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models. J Air Waste Manage Assoc 57:480–488

    Google Scholar 

  • Tootle GA, Singh AK, Piechota TC, Farnham I (2007) Long lead-time forecasting of U.S. streamflow using partial least squares regression. Eur J Soil Sci 12(5):442–451

    Google Scholar 

  • Uyak V, Ozdemir K, Toroz I (2007) Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs. Sci Total Environ 378(3):269–280

    Article  Google Scholar 

  • Vollenweider RA (1975) Input-output models, with special reference to the phosphorus loading concept in limnology. Schweiz Hydrol 37:53–84

    Article  Google Scholar 

  • Vryzas Z, Mourkidou EP, Soulios G, Prodromou K (2007) Kinetics and adsorption of metolachlor and atrazine and the conversion products (deethylatrazine, deisopropylatrazine, hydroxyatrazine) in the soil profile of a stream basin. Eur J Soil Sci 58(5):1186–1199

    Article  Google Scholar 

  • Welter JR, Fisher SG, Grimm NB (2005) Nitrogen transport and retention in an arid land watershed: influence of storm characteristics on terrestrial–aquatic linkages. Biogeochemistry 76(3):421–440

    Article  Google Scholar 

  • Xiao Y, Ferreira JG, Bricker SB, Nunes JP, Zhu M, Zhang X (2007) Trophic assessment in Chinese coastal systems—review of methods and application to the Changjiang (Yangtze) Estuary and Jiaozhou Bay. Estuaries Coasts 30(6):901–918

    Google Scholar 

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  1. Civil Engineering Department, Bogazici University, Bebek, 34312, Istanbul, Turkey

    Atilla Akkoyunlu & Muhammed Ernur Akiner

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  1. Atilla Akkoyunlu
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  2. Muhammed Ernur Akiner
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Correspondence to Atilla Akkoyunlu.

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Akkoyunlu, A., Akiner, M.E. Feasibility Assessment of Data-Driven Models in Predicting Pollution Trends of Omerli Lake, Turkey. Water Resour Manage 24, 3419–3436 (2010). https://doi.org/10.1007/s11269-010-9613-0

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  • DOI: https://doi.org/10.1007/s11269-010-9613-0

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