Abstract
AbstractThis research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in AI model outputs stems from errors in model input and nonuniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. The BAIMA employs a Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. The BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights are determined using the Bayesian information criterion (BIC) that follows the parsimony principle. The BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty because of model no...
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