Irrigation water quality evaluation using adaptive network-based fuzzy inference system

Water quality diagrams are comprised of quality classes defined by crisp sets, and as a consequence the boundaries between classes have an inherent imprecision. In this study, the concentration values of electrical conductivity (EC) and sodium adsorption ratio (SAR) in United States Salinity Laboratory diagram (USSL) are combined together through an adaptive network-based fuzzy inference system (ANFIS) to generate a new method that can be used instead of the USSL-diagram. The results showed that water quality classification based on the proposed method is more precise in comparison with the USSL-diagram classification, and it is a promising alternative to traditional approach. It has been observed that the ANFIS model with 96% accuracy has much better predicting capability than the Mamdani fuzzy inference system (MFIS). The results indicated that the ANFIS modeling decreases error effects in hydro-chemical experiments and it also significantly decreases computation time for the irrigation water quality evaluation.