Training for the test: Using multi-objective training to improve ANN ensemble forecasts of household residual chlorine in emergencies

Ensuring that sufficient free residual chlorine (FRC) persist in drinking water throughout the post-distribution period (collection, transport, and household storage) is critical to keeping drinking water safe in emergencies. Probabilistic models like artificial neural network (ANN) ensemble forecasting systems (EFS) have the potential to reproduce the high variability in post-distribution chlorine decay to generate risk-based chlorination guidance, but training with symmetrical error cost functions like mean squared error leads to poor probabilistic performance. This research proposes multi-objective (MO) training to improve the probabilistic performance of ANN-EFS forecasts of post-distribution FRC. Four MO optimizers were tested with combinations of seven objective functions and evaluated using water quality datasets from five emergency settings. MO training substantially improved probabilistic performance over conventional symmetrical error training. The solution that provided the most consistent improvement used preference-based optimization via backpropagation with the following objectives: similarity of mean, variance, and skew, correlation, recall, and precision. This approach achieved high performance at all sites and outperformed all baseline comparisons. These improved models will help humanitarian responders set informed chlorination targets that ensure water remains safe up to the point-of-consumption. This research highlights the importance of tailoring training approaches in ANN drinking water applications and hydroinformatics more broadly.