Simulation of water distribution under surface dripper using artificial neural networks

Predicting the wetting pattern of a dripper helps in the proper design of the drip irrigation system. An artificial neural network predictor model was developed based on the data from the well-tested model HYDRUS 2D/3D. The simulation data grid from HYDRUS was converted to simpler 3-variables vectors of wetting ellipses. The output vectors contain the radii in x and z directions and the center's location in the z direction. The simulations were performed for several textural classes, infiltration times, emitter's discharges, hydraulic models, and other features. After training the neural network, the testing dataset showed a correlation of 0.93-0.99, and the tested patterns showed high similarity to the HYDRUS outputs. Additionally, the paper provided solutions for the problem of simulating larger flow emitters where the flux exceeds the soil's hydraulic conductivity and the problem of converting HYDRUS outputs to easy-to-use vectors of three parameters representing specific moisture content at a particular time. This work tried a set of 51 input variables' permutations suggesting the best set of top results. The best trained neural network is freely available for the benefit of researchers and for future development. The sensitivity analysis of the input variables showed that the wetting pattern is mostly affected by time of infiltration, emitter discharge, and the saturated hydraulic conductivity. Future developments of the model are promising by increasing the training data extremes and possibly by adding more features like emitter's depth for the subsurface drippers.