@ARTICLE{Vadiati_Meysam_A_2019, author={Vadiati, Meysam and Nalley, Deasy and Adamowski, Jan and Nakhaei, Mohammad and Asghari-Moghaddam, Asghar}, volume={No 43}, pages={158-170}, journal={Journal of Water and Land Development}, howpublished={online}, year={2019}, publisher={Polish Academy of Sciences; Institute of Technology and Life Sciences - National Research Institute}, abstract={Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference sys-tems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydro-logical data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the pre-sent research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.}, type={Artykuły / Articles}, title={A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices}, URL={http://journals.pan.pl/Content/114839/PDF-MASTER/Vadiati%20et%20al%20Adamowski%20508.pdf}, keywords={fuzzy inference model, fuzzy rules, irrigation indices, Larson model, Mamdani model, Sugeno model, Sarab Plain}, }