How does the ANFIS model combine ANN and fuzzy logic, and what water quality parameters were used in the study mentioned?

ANFIS combines an artificial neural network's learning structure with fuzzy logic rules, using a fuzzy if-then inference system embedded in an ANN architecture to handle nonlinear and uncertain water-quality data. In the Kanchipuram temple pool study from 2017 to 2020, the parameters modelled were total dissolved solids, pH, chloride, sulphate, dissolved oxygen, the heavy metal iron, and total coliform count.

The chapter explains that ANFIS (Adaptive Neuro-Fuzzy Inference System) is a hybrid technique merging ANN and fuzzy logic. The ANN provides layered, interconnected neurons with entry, hidden, and exit layers, while fuzzy logic supplies if-then reasoning and the ability to manage uncertainty and misinformation in input data. This makes ANFIS effective for modelling nonlinear relationships such as surface-water quality. The water quality study described in the chapter used the WQI approach and considered total dissolved solids, pH, chloride, sulphate, dissolved oxygen, iron (a heavy metal), and total coliform count (a biological parameter) for the temple pool water.