  Continuous high frequency water quality monitoring is becoming a critical
task to support water management. Despite the advancements in sensor
technologies, certain variables cannot be easily and/or economically monitored
in-situ and in real time. In these cases, surrogate measures can be used to
make estimations by means of data-driven models. In this work, variables that
are commonly measured in-situ are used as surrogates to estimate the
concentrations of nutrients in a rural catchment and in an urban one, making
use of machine learning models, specifically Random Forests. The results are
compared with those of linear modelling using the same number of surrogates,
obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1%. The
profit from including up to seven surrogate sensors was computed, concluding
that adding more than 4 and 5 sensors in each of the catchments respectively
was not worthy in terms of error improvement.
