A novel approach in water quality assessment based on fuzzy logic

Abstract

The present work aimed at developing a novel water quality index based on fuzzy logic, that is, a comprehensive artificial intelligence (AI) approach to the development of environmental indices for routine assessment of surface water quality, particularly for human drinking purposes. Twenty parameters were included based on their critical importance for the overall water quality and their potential impact on human health. To assess the performance of the proposed index under actual conditions, a case study was conducted at Mamloo dam, Iran, employing water quality data of four sampling stations in the water basin of the dam from 2006 to 2009. Results of this study indicated that the general quality of water in all the sampling stations over all the years of the study period is fairly low (yearly averages are usually in the range of 45–55). According to the results of ANOVA test, water quality did not significantly change over time in any of the sampling stations (P > 0.05). In addition, comparison of the outputs of the fuzzy-based proposed index proposed with those of the NSF water quality index (the WQI) and Canadian Water Quality Index (CWQI) showed similar results and were sensitive to changes in the level of water quality parameters. However, the index proposed by the present study produced a more stringent outputs compared to the WQI and CWQI. Results of the sensitivity analysis suggested that the index is robust against the changes in the rules. In conclusion, the proposed index seems to produce accurate and reliable results and can therefore be used as a comprehensive tool for water quality assessment, especially for the analysis of human drinking water.