Data for: A Low-Complexity Machine Learning Nitrate Loss Predictive Model – Towards Proactive Farm Management in a Networked Catchment

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With the advent of Wireless Sensor Networks, the ability to predict nutrient-rich discharges, using on-node prediction models, offers huge potential for enabling real-time water reuse and management within an agriculturally-dominated catchment Existing discharge models use multiple parameters and large historical data which are difficult to extract and this, coupled with constraints on network nodes (battery life, computing power, sensor availability etc.) makes it necessary to develop low-dimensional models. This paper investigates a data-driven model for predicting daily total oxidized nitrate (TON) fluxes, and reduces the number of model parameters used to 5 – a reduction of at least 50%. Trained on only a 12-month training data set derived from published measured data, results for the model generated using an M5 decision tree, giving an R2 of 0.92 and a relative root mean squared error (RRMSE) of 26%. 80% of the residuals for test data fall within +/-0.05 Kg ha-1day-1 error range, which is minimal, offering an improvement over results obtained by contemporary research.


This is the data allowing some of the original figures to be reproduced.