Investigation of groundwater contamination level in Guilan province arising from Edifenphos (Hinosan) fungicide using a genetic algorithm
Paper Details
Investigation of groundwater contamination level in Guilan province arising from Edifenphos (Hinosan) fungicide using a genetic algorithm
Abstract
Contamination of groundwater of Guilan province with ediphenfos pesicide (hinosan) was investigated and the accuracy of its prediction was the studied using artificial neural network. Collection of data was performed from the entire province and their measurement lasted two years seasonally and at a single time for each season. The analysis method was in the form of liquid phase extraction together with gas chromatography with ECD detector. The modeling was performed using GMDH neural network considering two objective functions of training error and experimental error with optimization of the factors influencing the level of the concentration of ediphenfos toxin in groundwater of Guilan province. The parameters affecting the concentration of Ediphenfos toxin included the mean diameter of the particles, distance off the farms, well depth, pH, electrical conductivity, salinity, and level of precipitation. For optimization of the parameters, multi-objective genetic algorithm was used. Eventually, the degree of significance of each parameter in the prediction of toxin’s concentration was determined and the comparison of the results obtained from GMDH method with experimental data presented acceptable results. Considering the responsively of the model, it can be used for estimation of the concentration of other toxins as well.
Akbarizadeh M, Daghbandan A, Yaghoobi M. 2013. Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type-NN and MOGA. International Journal of Chemoinformatics and Chemical Engineering 3, 94-106.
Asadolah fardi GH, Taklifi I, Ghanbari A. 2010. The use of artificial neural network in the prediction of TDS in the Talkherod river. The Third Congress of Environmental Engineering, Tehran, Iran.
Asghari Moghadam A, Fijani E, Nadiri A. 2015. Optimization of Drastic Model by Artificial Intelligent for Groundwater Vulnerability Assessment in Maragheh-Bonab. Journal of Earth Sciences 24, 169-176.
Asghari Moghaddam A, Nadiri A, Fijani E. 2006. Ability to study different Models of Artificial Neural Networks to Evaluate Groundwater Water Level in the Hard Formation. Tenth Conference of Geological Society, Tehran, Iran.
Banihabibi MEB, Jamali F. 2010. Comparison of Dynamic Artificial Neural Network and Multivariable Linear Regression Model for Inflow Forecasting Using Remote Sensing. Journal of Soil and Water 20, 173-185.
Daghbandan A, Taleshi F, Yaghobi M. 2016. Comparison of Multi Objective GMDH-type Neural Network and Bayesian Belief Network in the Prediction of Treated Water Turbidity, Case Study: Great Water Treatment Plant in Guilan Province. Journal of Water and Wastewater 27, 71-83.
Engel T, Munch D. 1987. National Pesticide Survey Method 1, EPA Method 507. Pesticides,Capillary Column. EPA, Test Method for Drinking Water and Raw Source Water,1-6.
Esfandian H, Samadi-Maybodi A, Parvini M, Khoshandam B. 2016. Development of a novel method for the removal of diazinon pesticide from aqueous solution and modeling by artificial neural networks (ANN), Journal of Industrial and Engineering Chemistry 35, 295-308.
Ghanadzadeh H, Ganji M, Fallahi S. 2012. Mathematical model of liquid–liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm. Applied Mathematical Modeling 36, 4096-4105.
Chu H B， Lu W X, Zhang L. 2013. Application of Artificial Neural Network in Environmental Water Quality Assessment. J. Agr. Sci. Tech1 5, 343-356.
Heddam S, Bermad A, Dechemi N. 2012. ANFIS-based modeling for coagulant dosage in drinking water treatment plant: a case study. Environmental Monitoring and Assessment 184, 1953-1971.
Kheradpisheha Z, Talebib A, Rafatia L, Ghaneeiana MT. 2015. Ehrampousha Groundwater Quality Assessment Using Artificial Neural Network: A case study of Bahabad plain, Yazd, Iran. Desert 20-1, 65-1.
Jamali J, Abrishamchi A, Tajrishi M. 2008. River Stream-Flow and Zayanderoud Reservoir Operation Modeling Using the Fuzzy Inference System. Journal of Water and Wastewater 18, 25-34.
Lou I, Gong S, Huang X, Liu Y. 2012. Coagulation optimization for low temperature and low turbidity source water using combined coagulants: a case study. Desalination and Water Treatment 46, 107-114.
Maier HR, Morgan N, Chow CWK. 2004. Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environmental Modeling & Software 19, 485-494.
Mamdani EH. 1976. Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies 8, 669-678.
Mitchell M. 1998. An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts, London, England Fifth printing, 65- 87.
Moasheri SA, Khammar Gh A, Poornoori Z, Beyranvand Z, Soleimani M. 2013. Estimate the spatial distribution TDS the fusion method Geostatistics and artificial neural networks. International Journal of Agriculture and Crop Sciences 6, 410-420.
Noshadi M, Salami HR, Ahmadzadeh M. 2008. Simulation and Prediction of River Water Quality Parameters Using Artificial Neural Network. Journal of Water and Wastewater 18, 49-65.
Ostad Aliasgari K, Moazed H, Ghorbanizade H. 2012. Nitrate contamination in groundwater modeling zayandehrood using artificial neural network. The first regional conference of Civil Engineering, Tehran, Iran.
Sahoo GB, Chittaranjan R, Mehnert E, Keefer DA. 2006. Application of artificial neural networks to assess pesticide contamination in shallow groundwater, Science of The Total Environment 367, 234-251.
Shouliang Huo, Zhuoshi He, Jing Su, Beidou Xi, Chaowei Zhu. 2013 . Using artificial neural network models for eutrophication prediction. Procedia Environmental Sciences 18, 310 – 316.
Stenemo F, Lindhal A M, Gärdenäs A, Jarvis N. 2007. Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks, Journal of Contaminant Hydrology 93, 270-283.
Mir Moslem Rahbar Hashemi, Fariborz Jamalzad fallah, Mehdi Ashournia, Hadi Modaberi, Maryam Haghighi (2017), Investigation of groundwater contamination level in Guilan province arising from Edifenphos (Hinosan) fungicide using a genetic algorithm; JBES, V10, N4, April, P38-51
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