Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods
This work addresses water management challenges by providing a more accurate method for nutrient monitoring in rural and urban catchments, though it is incremental as it applies an existing ML method to a specific domain.
The paper tackled the problem of estimating high-frequency nutrient concentrations in water using machine learning on surrogate water quality variables, achieving up to a 60.1% reduction in RMSE compared to linear models.
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.