Photo: Nazeer Al-Khatib, AFP

Machine Learning for Safe Water

  • Institution Country test: Canada
  • Implementation Country: Bangladesh, Jordan, Yemen
  • Sector: Life Saving Information
  • Funding Stage: Proof of Concept

Machine Learning Enabled Safe Water Optimization Tool for Humanitarian Response

by the Dahdaleh Institute for Global Health Research, York University

Waterborne diseases are among the primary threats facing people affected by conflict, including those living in displacement camps, and where health infrastructures have been destroyed. Providing safe water is essential for limiting morbidity and mortality associated with waterborne diseases. Current guideline protocols for emergency water treatment are remarkably not based on field evidence, and often fail to ensure that water is safe to drink in emergency settings.

This project aims to build a novel safe water optimization tool, that leverages cloud computing and artificial intelligence to learn from the water quality monitoring data that humanitarian agencies already routinely collect for reporting purposes. Field workers will upload this routine data to a web-based platform in order to generate customized water chlorination instructions that are evidence-based, site-specific, and to demonstrably ensure that water is safe to drink, for any field site around the world.