Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Yas Barzegar, Irina Gorelova (), Francesco Bellini and Fabrizio D’Ascenzo
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Yas Barzegar: Department of Management, Sapienza University of Rome, 00161 Rome, Italy
Irina Gorelova: Department of Management, Sapienza University of Rome, 00161 Rome, Italy
Francesco Bellini: Department of Management, Sapienza University of Rome, 00161 Rome, Italy
Fabrizio D’Ascenzo: Department of Management, Sapienza University of Rome, 00161 Rome, Italy

IJERPH, 2023, vol. 20, issue 15, 1-20

Abstract: Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.

Keywords: water quality; drinking water; fuzzy logic; fuzzy inference systems; membership functions; water attribute; smart city (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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