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Investigation of groundwater contamination level in Guilan province arising from Edifenphos (Hinosan) fungicide using a genetic algorithm

Research Paper | April 1, 2017

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Mir Moslem Rahbar Hashemi, Fariborz Jamalzad fallah, Mehdi Ashournia, Hadi Modaberi, Maryam Haghighi

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J. Bio. Env. Sci.10( 4), 38-51, April 2017


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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.


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Investigation of groundwater contamination level in Guilan province arising from Edifenphos (Hinosan) fungicide using a genetic algorithm

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