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Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks
npj Clean Water ( IF 11.4 ) Pub Date : 2021-06-25 , DOI: 10.1038/s41545-021-00125-2
Michael De Santi , Usman T. Khan , Matthew Arnold , Jean-François Fesselet , Syed Imran Ali

Waterborne illnesses are a leading health concern in refugee and internally displaced person (IDP) settlements where waterborne pathogens often spread through household recontamination of stored water. Ensuring sufficient chlorine residual is important for protecting drinking water against recontamination and ensuring water remains safe up to the point-of-consumption. We used ensembles of artificial neural networks (ANNs) to probabilistically forecast the point-of-consumption free residual chlorine (FRC) concentration and to develop point-of-distribution FRC targets based on the risk of insufficient FRC at the point-of consumption. We built ANN ensemble models using data from three refugee settlements and found that the risk-based FRC targets generated by the ensemble models were consistent with an empirical water safety evaluation, indicating that the models accurately predicted the risk of low point-of-consumption FRC despite all ensemble forecasts being underdispersed even after post-processing. This demonstrates the usefulness of ANN ensembles for generating risk-based point-of-distribution FRC targets to ensure safe drinking water in humanitarian operations.



中文翻译:

使用人工神经网络集合预测难民定居点的消费点余氯

水源性疾病是难民和境内流离失所者 (IDP) 定居点的主要健康问题,在这些地方,水源性病原体通常通过家庭对储存水的再污染而传播。确保足够的余氯对于保护饮用水免受再污染和确保水在消费点之前保持安全非常重要。我们使用人工神经网络 (ANN) 的集合对消费点游离余氯 (FRC) 浓度进行概率预测,并根据消费点 FRC 不足的风险制定分配点 FRC 目标。我们使用来自三个难民定居点的数据构建了 ANN 集成模型,发现集成模型生成的基于风险的 FRC 目标与经验性水安全评估一致,表明模型准确地预测了低消费点 FRC 的风险,尽管所有集合预测即使在后处理后也未充分分散。这证明了 ANN 集合在生成基于风险的分配点 FRC 目标以确保人道主义行动中的安全饮用水方面的有用性。

更新日期:2021-06-25
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