Bayesian analysis of data-worth considering model and parameter uncertainties

The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, water flow and contaminant transport. As the collection of subsurface characterization and monitoring data is costly, it is imperative that the design of corresponding data collection schemes be cost-effective, i.e., that the expected benefit of new information exceed its cost. A major benefit of new data is its potential to help improve one’s understanding of the system, in large part through a reduction in model predictive uncertainty and corresponding risk of failure. Traditionally, value-of-information or data-worth analyses have relied on a single conceptual-mathematical model of site hydrology with prescribed parameters. Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty. This has led to a recent emphasis on conducting hydrologic analyses and rendering corresponding predictions by means of multiple models. We describe a corresponding approach to data-worth analyses within a Bayesian model averaging (BMA) framework. We focus on a maximum likelihood version (MLBMA) of BMA which (a) is compatible with both deterministic and stochastic models, (b) admits but does not require prior information about the parameters, (c) is consistent with modern statistical methods of hydrologic model calibration, (d) allows approximating lead predictive moments of any model by linearization, and (e) updates model posterior probabilities as well as parameter estimates on the basis of potential new data both before and after such data become actually available. We describe both the BMA and MLBMA versions theoretically and implement MLBMA computationally on a synthetic example with and without linearization.

Research highlights
► Data-worth analysis within a multimodel Bayesian framework.
► Compatible with maximum likelihood parameter estimation.
► Implemented on synthetic geostatistical example.