Date of Award
MSc in Data Analytics
This thesis evaluates and optimises a variety of predictive models for assessing biological classification status, with an emphasis on water quality monitoring. Grounded in previous pertinent studies, it builds on the findings of (Arrighi and Castelli, 2023) concerning Tuscany’s river catchments, highlighting a solid correlation between river ecological status and parameters like summer climate and land use. They achieved an 80% prediction precision using the Random Forest algorithm, particularly adept at identifying "good" ecological conditions, leveraging a dataset devoid of chemical data.
Martín Sánchez, Raúl, "Applying Machine Learning to Biological Status (QValues) from Physio-chemical Conditions of Irish Rivers" (2023). ICT. 40.