The cost of delivering the ML-derived preventative maintenance model compared to other models.

<p>The cost of delivering the ML-derived preventative maintenance model compared to other models.</p

Features and model outputs plotted as time series.

<p>All data is colored by field-verified failure status. Three features are plotted: number of events is the number of pumping events counted in a given day, while event deviation and flow deviation are normalized log-scale deviation of an event’s duration and flow from its expected duration (correcting for day of week). Following the features are the outputs of the current failure prediction, the forecast failure prediction, and the combined prediction (max of current and forecast). The y-axis of these metrics can be thought of as the probability that the pump has failed (current prediction), will fail (future forecast prediction), or has/will fail (combined prediction). For a threshold probability of 0.5, a future failure would be forecast the day before the true failure occurred for both the forecast classifier and the combined classifier.</p

Performance of the ensemble learner.

<p>Top: the Receiver Operating Characteristic (ROC) curve for both current and forecast failure prediction. The ROC represents the range of possible trade-offs between the classifiers’ true positive (truly failed pump classified as a failure) and false positive (truly functional pump classified as a failure) rates when choosing a threshold to operationalize the classifier. Bottom: the true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV, not to be confused with net present value) are plotted as a function of the learner’s probability threshold. This bottom panel illustrates the relationship between learner performance and the implementer-defined probability threshold to decide of a pump is broken (current) or will break (forecast) on any given day.</p

Achieving 99% uptime.

<p>The uptime of the pump fleet is plotted as a function of the number of dispatches per year for different dispatch delays. Achieving 99% uptime requires either a very short dispatch delay or many dispatches per year.</p

Learner performance for classifying current and forecasted failures.

<p>A solo GLM model is shown for reference against the ensemble model.</p

Primary stove adoption and use outcomes.

<p>*Only records within 1.5 IQR were included.</p

Reasons for stove and filter problems, repairs and replacements.

<p>*366 total filters repaired - 25 had multiple problems.</p

Figure 1

<p>Program villages. RCT villages shown with blue pins.</p

Primary filter adoption and use outcomes.

<p>*Only records within 1.5 IQR were included.</p

Selected demographics and characteristics regarding water and energy practices.

<p>*Missing 58 (3.6%) answers.</p