Reference evapotranspiration prediction using neural network method

Abstract

Water is the most vital resource for life on earth, At present we know that irrigation systems have currently acquired great importance due to the scarcity that is affecting worldwide, since there is no awareness about this important resource, however, for years we have worked to try to solve this problem. The objective of this research work was to develop a Feedforward Backpropagation type neural network algorithm with three layers: in the input layer include the operating factors such as the maximum temperature (°C), the minimum temperature (°C), the average temperature (°C) and solar radiation (mm / day) and the hidden layer three neurons and yet in the output layer only one neuron, this algorithm has been trained by the Levenberg-Marquardt algorithm to predict the evapotranspiration. The results were satisfactory because the algorithm was able to predict the reference evapotranspiration with a correlation coefficient of 99.99% and with an error of 0.0001. Therefore, this technique can be considered to automate the online irrigation system by monitoring plant transpiration and soil evaporation.