# Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

arXiv: https://arxiv.org/abs/2001.09695 · HTML: https://mlsanity.com/paper/2001.09695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning](https://mlsanity.com/paper/2501.07599/) (2025)
- [HydroSphere: A Framework for Governed, Self-Healing Wastewater Infrastructure](https://mlsanity.com/paper/2610.08819/) (2026)

## Abstract (arXiv)

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.