Investigating the effect of complexity on groundwater flow modeling uncertainty View Full Text


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Article Info

DATE

2017-07-12

AUTHORS

Saeideh Samani, Asghar Asghari Moghaddam, Ming Ye

ABSTRACT

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity. More... »

PAGES

643-659

References to SciGraph publications

  • 2003-11. Maximum likelihood Bayesian averaging of uncertain model predictions in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2010-03-30. Comparison of point forecast accuracy of model averaging methods in hydrologic applications in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 1985-09. Comparing strong and weak models by fitting them to computer-generated data in ATTENTION, PERCEPTION, & PSYCHOPHYSICS
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    http://dx.doi.org/10.1007/s00477-017-1436-6

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