Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare community, Southwestern Nigeria

•Water quality index (WQI) and fuzzy logic inference (FLI) assessed water quality.•FLI is more efficient, accurate, and affordable than WQI.•Waters in Ikare are generally “not suitable” for drinking.•The modeling confirms the water pollution as anthropogenic. 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, in assessing the water quality in Ikare community of Nigeria. Furthermore, crosstabs analysis and Python programming language validated 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, via the comparison, we infer on 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.