Adaptive neuro-fuzzy inference system for predicting norovirus in drinking water supply

Norovirus represent a leading cause of acute gastroenteritis in most developed countries. Modeling of general microbial occurrence in drinking water is a very active field of study and provides reliable information for predicting microbial risks in drinking water. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is proposed as a predicting model of the total number of Norovirus in raw surface water in terms of water quality parameters such as water pH, turbidity, conductivity, temperature and rain. ANFIS model combines in a transparent manner the linguistic representations of fuzzy logic and the learning capabilities of artificial neural networks. It also provides an automated rule generation and parameter optimization procedure that simplifies the complex process of model development and finally creates a transparent solution that is expected to offer useful insights into either the physical processes involved or the resultant modeling mechanisms. This study is based on the analysis of raw water samples from Nødre Romrike water treatment plant in Oslo, Norway. The ANFIS model has shown remarkable prediction ability of the number of Norovirus in drinking water with high accuracy. Significant relationship between Norovirus, turbidity, conductivity and temperature is obtained based on model results. The findings suggest that Norovirus outbreaks may be predicted based on certain environmental factors. The ability to predict the Norovirus in drinking water would make the prevention of potential outbreaks possible by applying the proper water treatment for better health risk management.