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Environmental Science and Pollution Research

, Volume 25, Issue 30, pp 30315–30324 | Cite as

Using the combined model of gamma test and neuro-fuzzy system for modeling and estimating lead bonds in reservoir sediments

  • Ali Akbar Mohammadi
  • Mahmood Yousefi
  • Jaber Soltani
  • Ahmad Gholamalizadeh Ahangar
  • Safoura Javan
Research Article
  • 35 Downloads

Abstract

Heavy metals attract a great deal of attention nowadays due to their potential accumulation in living creatures and transference in the food chain. Sediments of water reservoirs are considered to be a source of accumulation of these metals that develop in response to human activities and soil erosion. This study collected 180 samples of the surface sediments of water reservoir 1 at Chahnimeh in Sistan. Efficiency of the ANFIS model was evaluated to estimate the five bonds following the measurement of parameters in the laboratory.

The following results were obtained for the parameters: organic carbon (OC) %, 0.31; cation exchange capacity (CEC), 37.07 Cmol kg; total Pb, 25.19 mg/kg; clay %, 45.87; and silt %, 39.02. These parameters were used as input for the training model. In the output layer, lead bonds were chosen as modeling targets in the following way: Pb f1 (4.61); Pb f2 (0.54); Pb f3 (16.28); Pb f4 (3.42); and Pb f5 (0.38) mg/kg. The best input compound in this model was chosen using the gamma test. From a total of 180, 88 data were considered for the model training section. Eventually, the neural-fuzzy model (subtractive clustering), developed for the prediction of lead bonds in the studied region, was able to account for over 99% of lead bonds in the sediments; considering statistical criteria of root mean squares error or RMSE (0.0337–0.0813) and determination coefficient or R2 (0.92–0.99), this model showed good performance with regard to prediction.

Keywords

Sediments Gamma test M-test ANFIS Zabol, Iran 

Notes

Acknowledgements

The authors want to thank the authorities of Neyshabur University of Medical Sciences for their comprehensive support toward this study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abdolmaleki AS, Ahangar AG, Soltani J (2013) Artificial neural network (ANN) approach for predicting Cu concentration in drinking water of Chahnimeh1 reservoir in Sistan-Balochistan, Iran. Health Scope 2:31–38CrossRefGoogle Scholar
  2. Aboud S, Jumbe, Nandini N (2009) Heavy metals analysis and sediment quality values in urban lakes. Am J Environ Sci 5:678–687CrossRefGoogle Scholar
  3. Agarwal A, Mishra SK, Ram S, Sing JK (2006) Simulation of runoff and sediment yield using artificial neural networks. Biosyst Eng 94:597–613CrossRefGoogle Scholar
  4. Ahangar AG, Soltani J, Abdolmaleki AS (2013) Predicting Mn concentration in water reservoir using Artificial neural network (Chahnimeh1 reservoir, Iran). International Journal of Agriculture and Crop Sciences 6:1413–1420Google Scholar
  5. Ahangar AG, Sarani F, Hashemi M, Shabani A (2015) Comparison of linear regression methods, geostatistical and artificial neural network modeling of organic carbon in dry land of Sistan plain. Journal of Water and Soil 28:1250–1260Google Scholar
  6. Azamathulla H, Ghani A, Seow A (2012) ANFIS-based approach for predicting sediment transport in clean sewer. Appl Soft Comput 12:1227–1230CrossRefGoogle Scholar
  7. Badaoui HE, Abdallaoui A, Manssouri I, Lancelot L (2013) Application of the artificial neural networks of MLP type for the prediction of the levels of heavy metals in Moroccan aquatic sediments. International Journal of Computational Engineering Research| 3:75–86Google Scholar
  8. Bonakdari H, Baghalian S, Nazari F, Fazli M (2011) Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Engineering Application of Computational Fluid Mechanics 5:384–396CrossRefGoogle Scholar
  9. Corcoran J, Wilson I, Ware J (2003) Predicting the geo-temporal variations of crime and disorder. Int J Forecast 19:623–634CrossRefGoogle Scholar
  10. Defew LH, James MM, Hector MP (2005) An assessment of metal contamination in mangrove sediments and leaves from Punta Mala Bay, Pacific Panama. Mar Pollut Bull 50:547–552CrossRefGoogle Scholar
  11. Dinpashoh Y, Fakheri-Fard A, Moghaddam M, Jahanbakhsh S, Mirnia M (2004) Selection of variables for the purpose of regionalization of Iran’s precipitation climate using multivariate methods. J Hydrol 297:109–123CrossRefGoogle Scholar
  12. Duan W, He B, Takara K, Luo P, Nover D, Sahu N, Yamashiki Y (2013a) Spatiotemporal evaluation of water quality incidents in Japan between 1996 and 2007. Chemosphere 93:946–953CrossRefGoogle Scholar
  13. Duan W, Takara K, He B, Luo P, Nover D, Yamashiki Y (2013b) Spatial and temporal trends in estimates of nutrient and suspended sediment loads in the Ishikari River, Japan, 1985 to 2010. Sci Total Environ 461:499–508CrossRefGoogle Scholar
  14. Duan W, He B, Takara K, Luo P, Nover D, Hu M (2015) Modeling suspended sediment sources and transport in the Ishikari River basin, Japan, using SPARROW. Hydrol Earth Syst Sci 19:1295–1136Google Scholar
  15. Duan W, He B, Nover D, Yang G, Chen W, Meng H, Zou S, Liu C (2016) Water quality assessment and pollution source identification of the eastern Poyang Lake Basin using multivariate statistical methods. Sustainability 8:133CrossRefGoogle Scholar
  16. Evans D (2001) Data derived estimations of noise using near neighbor distance distributions. PhD Thesis, Cardiff University, Wales, U.K.Google Scholar
  17. Gallant S (1993) Neural network learning and expert systems. MIT press, CambridgeGoogle Scholar
  18. Hashemi M, Ahangar AG, Shabani A (2016) Evaluating pedotransfer functions for estimating ESP in the soils of Sistan plain. Journal of agricultural Engineenring 37:77–93Google Scholar
  19. Ibhadon AQ, Wright P, Daniels R (2004) Trace metal speciation and contamination in an intertidal estuary. Environ Monit Assess 6:679–683CrossRefGoogle Scholar
  20. Javan S, Hassani AH, Ahangar AG, Soltani J (2015) Fractionation of heavy metals in bottom sediments in Chahnimeh 1, Zabol, Iran. Environ Monit Assess 187(340):340CrossRefGoogle Scholar
  21. Jones A (2004) New tools in non-linear modeling and prediction. Comput Manag Sci 1:109–149CrossRefGoogle Scholar
  22. Kisi O, Ozkan C, Akay B, Vukovi J (2012) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428-429:94–103CrossRefGoogle Scholar
  23. Koncar N (1997) Optimization methodologies for direct inverse neurocontrol. PhD Thesis, Department of Computing, Imperial College of Science, Technology and Medicine, University of London, LondonGoogle Scholar
  24. Macfarlane GR, Burchett MD (2000) Cellular distribution of Cu, Pb and Zn in the grey mangrove Avicennia marina (Forsk.). Vierh Aquat Bot 68:45–59CrossRefGoogle Scholar
  25. Malakootian M, Khashi Z (2014) Heavy metals contamination of drinking water supplies in southeastern villages of Rafsanjan plain: survey of arsenic, cadmium, lead and copper. Journal of Health in the Field 2:1–9Google Scholar
  26. Manssouri I, Hmaidi AE, Manssouri TE, Moumni BE (2014) Prediction levels of heavy metals (Zn, Cu and Mn) in current Holocene deposits of the eastern part of the Mediterranean Moroccan margin (Alboran Sea). IOSR Journal of Computer Engineering 16:117–123CrossRefGoogle Scholar
  27. Mielke HW, Adams JL, Chaney RL, Mielkjr PW, Ravikumar VC (1991) The pattern of codmium in the environment of five Minnesota cities. Environ ,Geochem Health 13:29–34CrossRefGoogle Scholar
  28. Mir H, Ahangar AG, Shabani A (2016) Determination of the most important soil parameters affecting the availability of phosphorus in Sistan plain, using connection weight method in neural networks. Journal of Water and Soil 29:1674–1687Google Scholar
  29. Moghaddamnia A, Gousheh MG, Piri J, Han D (2008) Evaporation estimation using support vector machines technique. World Acad Sci Eng Technol 43:14–22Google Scholar
  30. Moghaddamnia A, Remesan R, Kashani MH, Mohammadi M, Han D, Piri J (2009) Comparison of LLR, MLP, Elman, NNARX and ANFIS models - with a case study in solar radiation estimation. J Atmos Sol Terr Phys 71:975–982CrossRefGoogle Scholar
  31. Moharrampour M, Kherad M, Abachi N, Zoghi M, Abad MAA (2012) Comparison of artificial neural networks ANN and statistics in daily flow forecasting. Adv Environ Biol 6:863–868Google Scholar
  32. Mosaferi M, Taghipour H, Hasani A, Borgheei M, Kordabad ZK, Ghadirzadeh A (2008) Study of arsenic presence in drinking water sources: a case study. Iranian Journal of Health and Environment 1:19–28Google Scholar
  33. Noori R, Karbassi A, Sabahi M (2009) Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. J Environ Manag 91:767–771CrossRefGoogle Scholar
  34. Pazhand HR (2001) Application of probability and statistics in water resources, 1st edn. Sokhan Gostar, Mashhad (in Persian)Google Scholar
  35. Piri A, Amin S, Moghaddamnia A, Keshavarz A, Han D, Remesan R (2009) Daily pan evaporation modeling in a hot and dry climate. J Hydrol Eng 14:803–811CrossRefGoogle Scholar
  36. Remesan R, Shamim M, Han D, Mathew J (2009) Runoff prediction using an integrated hybrid modeling scheme. J Hydrol 372:48–60CrossRefGoogle Scholar
  37. Sharifi A, Dinpashoh Y, Fakheri-Fard A, Moghaddamnia A (2014) Optimum combination of variables for runoff simulation in Amameh watershed using gamma test. Water and Soil Science 23:59–72Google Scholar
  38. Stefansson A, Koncar N, Jones A (1997) A note on the gamma test. Neural Comput & Applic 5:131–133CrossRefGoogle Scholar
  39. Tessier A, Campbell PG, Bisson M (1979) Sequential extraction procedure for the speciation of particulate trace metals. Anal Chem 51:844–851CrossRefGoogle Scholar
  40. WHO (2004) Guideline for drinking quality. 3rd edn, p 516Google Scholar
  41. WHO (2006) Air quality guidelines: global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health OrganizationGoogle Scholar
  42. Yang C, Marsooli R, Aalami MT (2009) Evaluation of total load sediment transport formulas using ANN. Int J Sediment Res 24:274–286CrossRefGoogle Scholar
  43. Yenigun K, Bilgehan M, Gerger, Re I, Mutlu M (2010) A comparative study on prediction of sediment yield in the Euphrates basin. International Journal of the Physical Sciences 5:518–534Google Scholar
  44. Zhang Y (2007) Artificial neural networks based principal component analysis input selection for clinical pattern recognition analysis. Talanta 73:68–75CrossRefGoogle Scholar
  45. Zhang YX, Li H, Hou A, Haval J (2006) Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks. Chemo Metrics and Intelligent Laboratory Systems 82:165–175CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ali Akbar Mohammadi
    • 1
  • Mahmood Yousefi
    • 2
  • Jaber Soltani
    • 3
  • Ahmad Gholamalizadeh Ahangar
    • 4
  • Safoura Javan
    • 1
  1. 1.Department of Environmental Health EngineeringNeyshabur University of Medical SciencesNeyshaburIran
  2. 2.Department of Environmental Health Engineering, School of Public HealthTehran University of Medical SciencesTehranIran
  3. 3.Irrigation and Drainage Engineering Department, Abouraihan Campus, University of TehranTehranIran
  4. 4.Department of Soil ScienceFaculty of Soil and Water University of ZabolZabolIran

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