Abstract
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.
Highlights
An increasing world population and climate change have placed a considerable amount of pressure on global freshwater supplies [1]
The data pre-processing steps rely on subjective user intervention, which limits the scalability of the models to new environments. This present study focuses on a dynamic modelling task, for which the Recurrent Neural Network (RNN) presents a suitable solution
The data applied in developing the neural network (NN) models for soil moisture prediction were obtained from three study sites, which are part of the Cosmic-Ray Soil Moisture Observing System (COSMOS) monitoring project in the United Kingdom [61]
Summary
An increasing world population and climate change have placed a considerable amount of pressure on global freshwater supplies [1]. It is desirable to irrigate to meet specific plant water demands at the right time while avoiding over and under irrigation. It is a processing element that takes a number of inputs, applies a weight to them, sums them up, includes a bias term, and passes the result to an activation function, which produces an output. This activation function implements a nonlinear transformation to the linearly combined input in order to produce a nonlinear output. The input–output relation of the system can be described by Equation (1) [45]:
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