Comparison of Geo-Statistical Methods and Artificial Neural Network in Estimating Groundwater Level (Case Study: Nourabad Plain, Lorestan)

Background and Geo-hydrology issues of changes in the water table are very important. Therefore research is necessary to estimate the missing data.
One of the important methods to estimate the groundwater table is interpolated. Recent decades due to the spatial correlation between the values ​​of a variable in a well developed area, geo-statistical science concepts and capabilities in the field of statistics to evaluate and predict the spatial variables expanded. In this study, the interpolation of groundwater level of Noorabad plains in the province of Lorestan, using geo-statistical methods, have been studied and the results were compared with conventional smart as artificial neural network. Measures average absolute error, mean bias error, root mean square error and standard deviation, and the methods used to assess the public.
The results showed that the spatial variation of groundwater table co-krigings simple circular model had a mean absolute error (0.0001), mean bias error (0.0347), root mean square error (0.0451m) and standard deviation (20.3) priority than other methods were.
Discussion and the results showed a high capacity co-krigings interpolation and prediction groundwater level is minimum and maximum values​​.