Meticulous prediction of hydrological processes, especially water budget has an individual importance in environmental management plans. On the other hand, conservation of groundwater, fundamental resources in arid and semi-arid areas, needs to be considered as a great priority in development sketches. Prediction of groundwater budget utilizing artificial intelligence was the scope of this study. For this aim, Azarshar Plain aquifer, East Azerbaijan, Iran was selected because of its great dependence on groundwater and the necessity of its budget cognition in future programs. The long-term fluctuations of the water table in 13 piezometers were simulated by wavelet-based artificial neural network hybrid model and their statistical gaps were covered. Then, the modelled water table was predicted for the next 12 months, using genetic programming. The results of simulation and prediction were assessed by performance evaluation criteria such as R2, root mean squared error, mean absolute error and Nash–Sutcliffe efficiency. Thiessen polygons were then utilized, plotting the predicted unit hydrograph of the study area. The predicted water table from September 2012 to August 2013 revealed about 0.12 m depletion. Regarding the area of Azarshahr Plain aquifer and its average storage coefficient, the aquifer budget will be reduced by about 0.3557 million cubic metres during this period.
- artificial intelligence
- genetic programming
- groundwater budget
- performance evaluation
- wavelet-artificial neural network
- First received 14 October 2015.
- Accepted in revised form 24 March 2016.
- © IWA Publishing 2016