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Published on September 6, 2022

Research by Dahdaleh Global Health Graduate Scholar Michael De Santi (lead author) and his coauthors, including DI Research Fellow Syed Imran Ali and DI Faculty Fellow Usman Khan, has recently been published in PLOS WATER – an open-access journal that brings together research relevant to the study of water, sanitation, and hygiene (WASH) and water resources for people and planet.

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

Abstract

Ensuring sufficient free residual chlorine (FRC) up to the time and place water is consumed in refugee settlements is essential for preventing the spread of waterborne illnesses. Water system operators need accurate forecasts of FRC during the household storage period. However, factors that drive FRC decay after water leaves the piped distribution system vary substantially, introducing significant uncertainty when modelling point-of-consumption FRC. Artificial neural network (ANN) ensemble forecasting systems (EFS) can account for this uncertainty by generating probabilistic forecasts of point-of-consumption FRC. ANNs are typically trained using symmetrical error metrics like mean squared error (MSE), but this leads to forecast underdispersion forecasts (the spread of the forecast is smaller than the spread of the observations). This study proposes to solve forecast underdispersion by training an ANN-EFS using cost functions that combine alternative metrics (Nash-Sutcliffe efficiency, Kling Gupta Efficiency, Index of Agreement) with cost-sensitive learning (inverse FRC weighting, class-based FRC weighting, inverse frequency weighting). The ANN-EFS trained with each cost function was evaluated using water quality data from refugee settlements in Bangladesh and Tanzania by comparing the percent capture, confidence interval reliability diagrams, rank histograms, and the continuous ranked probability. Training the ANN-EFS using the cost functions developed in this study produced up to a 70% improvement in forecast reliability and dispersion compared to the baseline cost function (MSE), with the best performance typically obtained by training the model using Kling-Gupta Efficiency and inverse frequency weighting. Our findings demonstrate that training the ANN-EFS using alternative metrics and cost-sensitive learning can improve the quality of forecasts of point-of-consumption FRC and better account for uncertainty in post-distribution chlorine decay. These techniques can enable humanitarian responders to ensure sufficient FRC more reliably at the point-of-consumption, thereby preventing the spread of waterborne illnesses.

De Santi M, Ali SI, Arnold M, Fesselet J-F, Hyvärinen AMJ, Taylor D, et al. (2022) Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts? PLOS Water 1(9): e0000040. 

Read the media release here.

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