%0 Journal Article %T Fluctuations in groundwater level prediction with Bayesian network (case study( %J Geographical Space %V 16 %N 56 %U http://geographical-space.iau-ahar.ac.ir/article-1-1381-en.html %R %D 2017 %K Groundwater, land statistics, Interpolation, Bayesian Network, Neural networks, %X Geohydrology issues ranging from the water table is very important . Therefore, research is necessary to estimate the missing data .In this study nourabad plain the 11 wells was observed that all the annual statistics of the statistical interpolation method ( Krigng and co ) places no statistics are calculated. This study aims to predict the groundwater level fluctuations , using intelligent Bayesian networks and artificial neural networks has been modeled .For this purpose, the latitude and longitude on a monthly time scale as the input and output parameters were selected as the groundwater level fluctuations .Criteria of correlation coefficient , root mean square error and coefficient of Nash suttclif and performance models were used to assess. The results showed that the neural network model has a correlation coefficient ( 0.880 ) , root mean square error ( 0.024m) and the standard Nash suttclif ( 0.900 ) in step verification succeeded with considerable accuracy to estimate water level fluctuations would . %> http://geographical-space.iau-ahar.ac.ir/article-1-1381-en.pdf %P 185-200 %& 185 %! %9 Research %L A-10-1635-3 %+ %G eng %@ 1735322X %[ 2017