Chloé Poulin

Researcher - AI for Agricultural Water Management

Chloé Poulin has six years of experience in the WASH sector, where she developed machine-learning models to predict groundwater microbial quality at the national scale in Uganda and to identify poor households eligible for water subsidies in Ghana. She then worked in a start-up where she developed a groundwater-level predictive model for the Central Valley, California (USA). At IWMI, Poulin is currently working on understanding water pumping behavior using IoT systems that record groundwater extraction from solar pumps in Kenya. This includes developing machine-learning models for pump clustering and cluster-level predictions, integrating environmental variables such as evapotranspiration, precipitation and land use/land cover. In parallel, she is developing machine learning models to predict groundwater salinity in the Chtouka-Massa Aquifer in Morocco, supporting government efforts to strengthen sustainable groundwater management and irrigation planning