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 R 2 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 -1 ůday -1 error range, which is minimal, offering an improvement over results obtained by the contemporary research.
Highlights
Fertilizers rich in phosphorus (P), potassium (K) and nitrogen (N) are added to soil to increase crop yields
Unlike P, which is less soluble than N (which in the form of nitrates and nitrites, is referred to as total oxidized nitrogen (TON)), N is more prone to be lost through leaching and drainage water [2]–[4]
In this paper we extend the concept of abstraction used in [43] and extend on [34] to further simplify the model parameters with a view towards eventual deployment within a wireless sensor networks (WSNs)
Summary
Fertilizers rich in phosphorus (P), potassium (K) and nitrogen (N) are added to soil to increase crop yields. MODEL DEVELOPMENT OF A LOW-COMPLEXITY TON-LOSS PREDICTIVE MODULE As discussed in the introduction, the adoption of WSNs for nutrient management in general and the implementation of WQMCM framework on a farm, requires simplified predictive models based on fewer, and ideally, real-time field information acquired autonomously and shared by the neighboring farms.
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