Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare community, Southwestern Nigeria
By J. O. Oladipo, A. Akinwumiju, O. Aboyeji, A. Adelodun. Environmental Challenges, 2021
57 citations recorded by the source corpus.
Abstract A ubiquitous, reliable, and affordable method to determine surface water quality (SWQ) is still evasive. In the current study, we compared two easily accessible statistical tools: fuzzy logic (FL) inference and water quality index (WQI) analytical methods, to assess the water quality in Ikare community of Nigeria. Further, crosstabs analysis and Python programming language were used to validate the reliability of FL and WQI methods. Consequently, a classification map was generated in the ArcGIS environment to depict the disparity in the methods' pollution distribution levels. We observed that, of the 20 sampling points, BOD5 evinced a 65% and 37% impact on SWQ via WQI and FL analyses, respectively. However, with fecal coliform, FL and WQI professed an absolute (100%) and zero (0%) impact, respectively. Through ArcGIS, 8.3% of the waters were categorized as "moderate" for drinking using FL, whereas WQI classified the whole study area's waters as "bad". Hence, by the comparison, we observed the superiority of FL inference over WQI methods because FL enables equal consideration to the measured values and SWQ standards, whereas WQI considers only the latter for evaluation.
Subfields: Environmental Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/26acaf654580a982f640b3aedad763540d0c0aec
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In Environmental Engineering
In Water Science and Technology
Citations in the corpus
- A review of the application of machine learning in water quality evaluation (2022, methodology): “To date, many methods have been developed to monitor and assess water quality worldwide, such as the multivariate statistical method [2], fuzzy inference [3], and the water quality index (WQI) [4].”