Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

Abstract

Disinfecting water with chlorine is crucial for maintaining sanitation and preventing waterborne diseases. However, managing Free Residual Chlorine (FRC) concentrations becomes challenging in water supply systems, as its levels can decrease throughout the distribution network, impacting water quality and public health. By leveraging water quality data from the Water Quality Surveillance Information System for Human Consumption (Sisagua) and employing the variable selection method t-Distributed Stochastic Neighbor Embedding, we crafted a Multilayer Perceptron (MLP) neural network. This MLP, fine-tuned with weighted mean square error (WMSE), was designed to evaluate the decline of free residual chlorine in water distribution networks within rural Brazilian communities. Training the model with the weighted mean squared error (WMSE) increased the average hit rate from 3.6 % to 5.0 %. In contrast to the traditional colorimetric method for predicting free residual chlorine, the proposed methodology showed superior performance, with accuracy rates of 85.45 %, 95.58 % and 99.84 % for equipment accuracies of 0.5 mg. /L, 1.0 mg/L and 2.0 mg/L, respectively. The MLP approach not only anticipates nonconformities related to FRC but also aids in adjusting chlorine dosage proactively.