Simo Ylönen

Simo Ylönen's doctoral research concerns quality assurance of hydrological monitoring. He develops machine learning methods that automatically detect errors and anomalies in sensor-level observations from national monitoring networks, fill gaps in the records, and assure data quality in real time. The same problems recur everywhere: missing observations, sensor faults, and limited resources for manual checking. The methods are also applied to flood and snow impact forecasting. The work began at the Finnish Environment Institute and continues at Oulu in the Digital Waters doctoral pilot.

I will make your numbers to be what you think they already are.