SBIR Phase I: Predictive Algorithms for Water Point Failure

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in creating a market for financially sustained and accountable water services in developing countries. The impact of improved water, sanitation, and hygiene on public health is significant, and has the potential to prevent at least 9.1% of the global disease burden and 6.3% of all deaths. Present-day approaches for delivering water services in developing countries typically focus on deploying, maintaining, and monitoring aid-projects for only a few years. Impact is nominally evaluated by implementers (non-profit, private and government alike) directly. However, even when a positive impact is measured, the majority of these environmental service and monitoring interventions are short-term, and measurements may be misleading. For example, a multi-decade project apparently increased access to clean water supplies in rural areas from 58% in 1990 to 91% in 2015. Improved services may be realized through preventative and "just in time" maintenance activities, enabled through instrumentation and predictive failure data analysis algorithms. This may, critically, enable zero-interruption in water supply. Intermediate access to water, caused by water point failure, to clean water is known to increase health risks. This Small Business Innovation Research (SBIR) Phase I project intends to develop predicative machine learning algorithms for water point failures derived from cellular reporting electronic sensors installed on rural water infrastructure in developing countries. The innovation proposed for research in this proposal consists of employing an ensemble of robust machine learning classification techniques, using cross-validation methods to tune model parameters and evaluate performance, in order to develop a data-adaptive system capable of predicting failure well enough in advance to allow preventive maintenance, repair or replacement. Specifically, we will first examine condition based maintenance. Condition based maintenance has several advantages over time based maintenance, especially the ability to allocate limited maintenance resources where they are needed, instead of spreading maintenance resources evenly, including where they may not be needed. Our proposed Phase 1 SBIR focuses on developing predictive algorithms for water point failures using our existing sensor hardware and applied to existing customers. Our success criteria for a Phase 1 SBIR is a predictive algorithm that can accurately identify water points in near-failure.