Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
International Journal of Human-Computer Studies
Reservoir operation using the neural network and fuzzy systems for dam control and operation support
Advances in Engineering Software
Spatial estimation model of porosity
Computers & Geosciences
Use of hybrid intelligent computing in mineral resources evaluation
Applied Soft Computing
Evaluation of Mn concentration provided by soil in citrus-growing regions
Computers and Electronics in Agriculture
Comparison of spatial interpolation methods for estimating heavy metals in sediments of Caspian Sea
Expert Systems with Applications: An International Journal
Hydraulic head interpolation using anfis-model selection and sensitivity analysis
Computers & Geosciences
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Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical conductivity based on the concentration of positively charged ions in water. It is shown that the ANFIS model outperforms more traditional methods of modelling electrical conductivity based on the total solids dissolved in the water, even though ANFIS uses less information. Additionally, the fuzzy rules in the ANFIS model provide a categorization of ground water samples in a manner that is consistent with the current understanding of geophysical processes.