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Mehdi Kord

Mehdi Kord

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId: 56099782400
HIndex:
Faculty: Faculty of Science
Address: Iran, Kurdistan, Sanandaj, Pasdaran street, University of , Kurdistan, Faculty of Science, Department of Earth Science, Postal code : 66177-15177
Phone: 09188878406

Research

Title
Fuzzy logic: A suitable tool for determining water quality based on quality standards
Type
Speech
Keywords
Fuzzy Logic, Uncertainty, Water quality, Zagros
Year
2022
Researchers Mehdi Kord

Abstract

This speech is about the ability of fuzzy logic to assess water quality and reduce uncertainty, along with several case studies in the Zagros zone. Water plays a vital role in human life, and its deficiency directly affects all aspects of man’s life. In arid regions, with low shares of precipitation and surface water, groundwater is a highly crucial resource. The Middle East is a region with low precipitation and severe rainfall fluctuations. This increase the importance of water in this area. Water quality and water quantity are important. Physical, chemical, and biological parameters are measured and compared with the quality limits set in the water quality standard to determine water quality. Various quality standards are varied in specified limits to measure water quality. On the other hand, measuring qualitative parameters is always associated with uncertainty. Therefore, determining a water quality has always been challenging in cases on the border of two quality limits. Quality assessment requires several qualitative parameters, it may be difficult to examine the parameters one by one over time and make decisions about them. Hence, calculation of the water quality indices can be very helpful to express the quality of water as a number to simplify the task, and it makes it easier to judge. Fuzzy logic reduces uncertainty by considering membership functions and acts like an expert. The results of calculating the water quality indices by fuzzy logic show the ability of this method to examine a large number of samples.