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

Abstract

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, and sensor availability), makes it necessary to develop low-dimensional models. This paper investigates a data-driven model for predicting daily total oxidized nitrate fluxes and reduces the number of model parameters used to 5-a reduction of at least 50%. Trained on only a 12-month training dataset derived from the published measured data, results for the model generated using an M5 decision tree, giving an R2 of 0.92 and a relative root-mean-square error of 26%. The 80% of the residuals for test data falls within +/-0.05 Kgůha-1ůday-1 error range, which is minimal, offering an improvement over results obtained by the contemporary research.

Keywords

• Environmental modeling
• nitrate loss prediction modeling
• machine learning
• M5 decision tree
• wireless sensor networks
• water quality management