UW Applied Physics Laboratory (APL)

Enabling a Novel Low-Cost Salinity Sensor through Machine Learning

This team developed a robust machine-learning framework that corrects long-term drift in low-cost salinity sensors. Salinity data is vital for monitoring ocean health, tracking climate change, and managing sustainable aquaculture, yet high-quality equipment is often very expensive. While these low-cost sensors offer a scalable alternative compared to more expensive conductivity-temperature-depth instruments, they suffer from physical degradation like membrane hydration and ionophore leaching. By analyzing time-series data, which includes features such as voltage, impedance, and temperature, this model automatically separates true environmental variations from apparent changes caused by sensor drift.