A Bayesian network approach for predicting groundwater level (Case study: Qazvin aquifer)

Excessive use of groundwater resources has put the aquifers in critical situations. This study provides a framework for using the Bayesian network for groundwater level estimation and aquifer hydrograph analysis. A 10-year statistical data, 8 years data for model training, and 2 years data for model validation, were used. The Bayesian network model was implemented and analyzed in three explicit, clustering, and two- and three-month delay states. Explicit simulation results showed that most of the wells have a good correlation between the simulation and observed data. Clustering results were less accurate than explicit ones. In the third case, two and three months delay data was used to simulations. The results showed that the correlation between observed and simulated groundwater levels decreased. At 1, 2 and 3 months delay statues, Root Mean Square Error was 1.87 m, 3.76 m, and 6.42 m, respectively. Therefore, the one-month lag time was chosen for the simulations and aquifer hydrograph was used to evaluate and estimate total aquifer variations. The results indicate the appropriate accuracy of the aquifer parameters estimation.