Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps2018 Mar 25
Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps
Abstract: Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers’ health benefits and willingness to pay for clean water are best realized when clean water infrastructure performs extremely well (>99% uptime). In this paper, we used sensor data from 42 Afridev-brand handpumps observed for 14 months in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly. Comparing the costs of operating the pump per functional year over a lifetime of 10 years, we estimate that implementing this algorithm would save 7% on the levelized cost of water relative to a sensor-less scheduled maintenance program. Combined with a rigorous system for dispatching maintenance personnel, implementing this algorithm in a real-world program could significantly improve health outcomes and customers’ willingness to pay for water services.
Discussion: From a program viewpoint, implementers are primarily interested in increasing reliable and cost-effective water services. Fig 4 illustrates the trade-off between fleet uptime and dispatch responsiveness as a function of the number of model-initiated dispatches per pump-year. The figure is faceted by dispatch delays ranging from 1 to 21 days. There are two important insights visible in this figure. First, on a per-dispatch perspective, there is very little difference between current, forecast, and combined models. The current failure model typically performs slightly better on a per-dispatch basis (as a result of its higher positive predictive value). However, the most important difference in fleet uptime results from the implementing agency’s dispatch delay, and, to a lesser extent, the implementing agency’s capacity to perform many dispatches in a pump-year. The goal of 99% fleet uptime could be achieved with our machine learning model using just 2 dispatches per pump-year paired with a 1-day dispatch delay, or 22 dispatches per pump-year with a 7-day dispatch delay.
The marginal cost of implementing sensors, machine learning, and preventative maintenance activity are spread over the total utility that the equipment (a handpump in this case), delivers to customers over its lifetime. For this reason, there would be an even greater per-dollar benefit from implementing a sensor and machine learning-enabled preventative maintenance program on larger commercial assets such as motorized borehole pumping stations. While the cost of sensors and algorithms would not be significantly changed, the total benefit delivered to customers per functional pump-year would be greatly increased because of the larger pumping capacity of these stations.
In conclusion, the highly non-linear relationship between pump performance and health & economic outcomes illustrates that pumps need to perform extremely well before their benefits to society can be realized. This non-linear relationship also suggests that there is more consumer surplus to be gained by improving the function of existing pumps rather than building ever more new pumps that function only marginally well. This study has demonstrated that a machine-learning-enabled preventative maintenance model has the potential to enable fleets of handpumps that function extremely well by driving total fleet uptime to >99%, thus providing a realistic path forward towards reliable and sustained clean water delivery.