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:: Volume 16, Number 56 (3-2017) ::
2017, 16(56): 185-200 Back to browse issues page
Fluctuations in groundwater level prediction with Bayesian network (case study(
Abstract:   (595 Views)

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 .

Keywords: Groundwater, land statistics, Interpolation, Bayesian Network, Neural networks
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Type of Study: Research | Subject: Special
Received: 2014/12/26 | Accepted: 2015/08/31 | Published: 2017/02/26
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Fluctuations in groundwater level prediction with Bayesian network (case study(. Geographical Space. 2017; 16 (56) :185-200
URL: http://geographical-space.iau-ahar.ac.ir/article-1-1381-en.html
Volume 16, Number 56 (3-2017) Back to browse issues page
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