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A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices

Authors
  • Vadiati, Meysam1
  • Nalley, Deasy1
  • Adamowski, Jan1
  • Nakhaei, Mohammad2
  • Asghari-Moghaddam, Asghar3
  • 1 Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, Canada , (Canada)
  • 2 Department of Applied Geology, Faculty of Earth Sciences , (Iran)
  • 3 Department of Earth Sciences, Faculty of Natural Science , (Iran)
Type
Published Article
Journal
Journal of Water and Land Development
Publisher
Walter de Gruyter GmbH
Publication Date
Dec 01, 2019
Volume
43
Issue
1
Pages
158–170
Identifiers
DOI: 10.2478/jwld-2019-0074
Source
De Gruyter
Keywords
License
Green

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 systems (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 hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present 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.

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