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.
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References
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 2:31–38
Aboud S, Jumbe, Nandini N (2009) Heavy metals analysis and sediment quality values in urban lakes. Am J Environ Sci 5:678–687
Agarwal A, Mishra SK, Ram S, Sing JK (2006) Simulation of runoff and sediment yield using artificial neural networks. Biosyst Eng 94:597–613
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 6:1413–1420
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 28:1250–1260
Azamathulla H, Ghani A, Seow A (2012) ANFIS-based approach for predicting sediment transport in clean sewer. Appl Soft Comput 12:1227–1230
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| 3:75–86
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 5:384–396
Corcoran J, Wilson I, Ware J (2003) Predicting the geo-temporal variations of crime and disorder. Int J Forecast 19:623–634
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 50:547–552
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 297:109–123
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 93:946–953
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 461:499–508
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 19:1295–1136
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 8:133
Evans D (2001) Data derived estimations of noise using near neighbor distance distributions. PhD Thesis, Cardiff University, Wales, U.K.
Gallant S (1993) Neural network learning and expert systems. MIT press, Cambridge
Hashemi M, Ahangar AG, Shabani A (2016) Evaluating pedotransfer functions for estimating ESP in the soils of Sistan plain. Journal of agricultural Engineenring 37:77–93
Ibhadon AQ, Wright P, Daniels R (2004) Trace metal speciation and contamination in an intertidal estuary. Environ Monit Assess 6:679–683
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):340
Jones A (2004) New tools in non-linear modeling and prediction. Comput Manag Sci 1:109–149
Kisi O, Ozkan C, Akay B, Vukovi J (2012) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428-429:94–103
Koncar N (1997) Optimization methodologies for direct inverse neurocontrol. PhD Thesis, Department of Computing, Imperial College of Science, Technology and Medicine, University of London, London
Macfarlane GR, Burchett MD (2000) Cellular distribution of Cu, Pb and Zn in the grey mangrove Avicennia marina (Forsk.). Vierh Aquat Bot 68:45–59
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 2:1–9
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 16:117–123
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 13:29–34
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 29:1674–1687
Moghaddamnia A, Gousheh MG, Piri J, Han D (2008) Evaporation estimation using support vector machines technique. World Acad Sci Eng Technol 43:14–22
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 71:975–982
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 6:863–868
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 1:19–28
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 91:767–771
Pazhand HR (2001) Application of probability and statistics in water resources, 1st edn. Sokhan Gostar, Mashhad (in Persian)
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 14:803–811
Remesan R, Shamim M, Han D, Mathew J (2009) Runoff prediction using an integrated hybrid modeling scheme. J Hydrol 372:48–60
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 23:59–72
Stefansson A, Koncar N, Jones A (1997) A note on the gamma test. Neural Comput & Applic 5:131–133
Tessier A, Campbell PG, Bisson M (1979) Sequential extraction procedure for the speciation of particulate trace metals. Anal Chem 51:844–851
WHO (2004) Guideline for drinking quality. 3rd edn, p 516
WHO (2006) Air quality guidelines: global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization
Yang C, Marsooli R, Aalami MT (2009) Evaluation of total load sediment transport formulas using ANN. Int J Sediment Res 24:274–286
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 5:518–534
Zhang Y (2007) Artificial neural networks based principal component analysis input selection for clinical pattern recognition analysis. Talanta 73:68–75
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 82:165–175
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The authors want to thank the authorities of Neyshabur University of Medical Sciences for their comprehensive support toward this study.
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Mohammadi, A., Yousefi, M., Soltani, J. et al. Using the combined model of gamma test and neuro-fuzzy system for modeling and estimating lead bonds in reservoir sediments. Environ Sci Pollut Res 25, 30315–30324 (2018). https://doi.org/10.1007/s11356-018-3026-7
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DOI: https://doi.org/10.1007/s11356-018-3026-7