Feed Forward Neural Networks and Backpropagation for Prediction and Estimation of Evaporation in Water Reservoir

While there are methods to estimate the amount of evaporation, there were no mentions in recent studies of predicting it. In this paper, evaporation in a water reservoir for the succeeding 24 hours is predicted using a feed forward artificial neural network and backpropagation. The neural network is trained using historical data with temperature, humidity, wind speed, and solar radiation as input parameters. Various configurations of the neural network is considered and the best architecture is selected accordingly. The same weather parameters used in the training are gathered using sensors and are used as inputs to predict of the evaporation over the succeeding 24 hours. The predicted or the experimental value of evaporation is then compared to the evaporation estimated using a class A pan. The number of neurons in the hidden layer is varied from two to twelve and is evaluated by mean squared error. 4-7-1, the hidden layer with seven neurons is selected. In testing the accuracy in predicting the evaporation, it resulted to an average error of 4.48%.