Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

The relation between water table depth and evaporation rate from bare soil is of great importance in arid and semi-arid areas. In such areas, due to over irrigation, the water table is very close to the ground surface which leads to salinization of the soil. In this study a physical water table model was used to estimate evaporation rate in sandy loam, loam and clay loam soils under greenhouse conditions for 40, 60 and 80 cm water table depth. The evaporation from bare soil, evaporation from free surface, soil surface moisture (with using TDR) and maximum and minimum daily temperature were measured for 74 days in this study. In the next step, several nonlinear models have been efficiently developed with the aid of the Gamma test (GT), including local linear regression, two layer back propagation, and conjugate gradient descent and BFGS neural network to simulate the evaporation from soil. And finally, for evaluation of the models, the root mean square error and mean absolute error and larger determination coefficient were calculated. The results showed a suitable correlation between the predicted values and the test measures.