Assessing water quality in rivers with fuzzy inference systems: A case study

Measuring energy security performance within China: Toward an inter-provincial prospective

“…Fuzzy AHP is frequently used to capture expert knowledge of a preference numerically, and applied to solving hierarchical and complex decisionmaking problems 85 . Fuzzy AHP uses a membership function to calculate a grade of membership that a given variable belongs to, and triangular and trapezoidal functions are usually used in fuzzy logic because they are simple to use but also accurate [86][87][88][89][90] . The PROMETHEE method is one of the most recent MCDA techniques, firstly developed by Brans 91 .…”

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

“…A combination of AHP and FIS has also been employed in several applications (Carreño Donevska et al, 2011;Nilashi et al, 2015;Rodríguez et al, 2016). A fuzzy logic inference system is a process from a given input of empirical values to an output including three main parts (Carbajal-Hernández et al, 2012b;Jamshidi et al, 2013;Ocampo-Duque et al, 2006;Ross, 2004): (1) membership functions; (2) fuzzy set operations; (3) IF-THEN inference rules. A Mamdani-type inference system (Mamdani & Assilian, 1975) is used in our model because of the more intuitive and human-like nature of its rules compared to other types (Che Osmi et al, 2016;Kovac et al, 2012).…”

“…FIS has been used in many applications since then. It has been applied in environmental models including: waste management (Vesely et al, 2016) forecasting air quality (Carbajal-Hernández et al, 2012a;Fisher, 2006;Sowlat et al, 2011) water quality (Carbajal-Hernández et al, 2012b;Che Osmi et al, 2016;Gharibi et al, 2012;Ocampo-Duque et al, 2006) models for performing risk assessment (Camastra et al, 2015;Jamshidi et al, 2013;Rodríguez et al, 2016) in the field of manufacturing and sales for supporting customers' requirements (Juang et al, 2007) forecasting automobile sales (Wang et al, 2011) stock price prediction (Chang & Liu, 2008) supplier selection (Lima Junior et al, 2013) measuring customer satisfaction (Zani et al, 2013). Similarly to our model, FIS has been applied in models for evaluating the performance level of several fields (Nadali et al, 2011;Nilashi et al, 2015).…”

Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd province, Iran

“…In turn, some parameters in the index equation can influence dramatically the final score without valid justification, while their formulations are rather elementary, and the number of variables involved is too limited. However, the most critical deficiency of these indices is the lack of dealing with uncertainty and subjectivity present in this complex environmental problem [8]. Along with the limitations of these methods, conventional water quality regulation proposed by various regulatory bodies like Word Health Organization (WHO), Institute of Standards and Industrial Research of Iran (ISIRI) contain quality classes which use crisp sets, and the limits between different classes have inherent imprecision [15].Furthermore, to monitor water quality and to make qualitative and quantitative decisions based on real data has become a challenge for environmental engineers and hydrogeologist over all stages of the process, from data collection, storage and processing up to analysis and interpretation of the results.…”