Soil & Water Res., 2012, 7(2):73-83 | DOI: 10.17221/46/2010-SWR

Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference systemOriginal Paper

Sayed Farhad MOUSAVI, Mohammad Javad AMIRI
Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran

High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R2 = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R2 = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.

Keywords: Adaptive Neural-Based Fuzzy Inference System; nitrate pollution; water quality parameters

Published: June 30, 2012  Show citation

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MOUSAVI SF, AMIRI MJ. Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system. Soil & Water Res. 2012;7(2):73-83. doi: 10.17221/46/2010-SWR.
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