Engineering Commons^™

Ensemble Machine Learning And Forecasting Can Achieve 99% Uptime For Rural Handpumps, Daniel L. Wilson, Jeremy R. Coyle, Evan A. Thomas

Mechanical and Materials Engineering Faculty Publications and Presentations

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 …