Paper Information

Journal:   WATER ENGINEERING   SPRING 2011 , Volume 4 , Number 8; Page(s) 11 To 20.
 
Paper: 

PREDICTION OF EVAPORATIVE FLUX FROM SHALLOW WATER TABLES USING REGRESSION AND ARTIFICIAL NEURAL TECHNIQUES

 
Author(s):  CHARI M.M.*, AFRASYAB P., PIRI J., DELBARI M.
 
* INTERNATIONAL HAMOUN WETLAND, ZABOL UNIVERSITY
 
Abstract: 

Knowledge of the relationships between the watertable depth and evaporation rate from the bare soil is of immense importance in arid and semi arid area. The rise in watertable, land inundation, and soil salinization are inevitable aftermath of over-irrigation in such environments. Evaporative flux was measured in 200 mm inside diameter PVC columns filled with the sandy loam, loam and clay loam soils, maintaining the watertable in them at a depth of 40, 60 and 80 cm. Evaporation from both the bare soil and exposed water surface, soil water content, and the maximum and minimum air temperatures were measured daily for 74 consecutive days. The TDR technique was used for the soil water content determination. Several nonlinear models were developed using benefiting from the gamma test (the Win Gamma program), including the local linear regression, the 2-layer back propagation, conjugate gradient descent, and the BFGS neural network. Validity of these models was ascertained using the root mean square error, the mean absolute error, and the larger coefficient of determination. The models satisfactorily predicted the measured evaporative flux.

 
Keyword(s): EVAPORATION FROM THE BARE SOIL, GAMMA TEST, REGRESSION MODEL, NEURAL NETWORKS
 
References: 
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