# Surrogate modeling and risk-based analysis for solute transport simulations

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## Abstract

This study is driven by the question of how quickly a solute will be flushed from an aquatic system after input of the solute into the system ceases. Simulating the fate and transport of a solute in an aquatic system can be performed at high spatial and temporal resolution using a computationally demanding state-of-the-art hydrodynamics simulator. However, uncertainties in the system often require stochastic treatment and risk-based analysis requires a large number of simulations rendering the use of a physical model impractical. A surrogate model that represents a second-level physical abstraction of the system is developed and coupled with a Monte Carlo based method to generate volumetric inflow scenarios. The surrogate model provides an approximate 8 orders of magnitude speed-up over the full physical model enabling uncertainty quantification through Monte Carlo simulation. The approach developed here consists of an stochastic inflow generator, a solute concentration prediction mechanism based on the surrogate model, and a system response risk assessment method. The probabilistic outcome provided relates the uncertain quantities to the relevant response in terms of the system’s ability to remove the solute. We develop a general approach that can be applied in a generality of system configurations and types of solute. As a test case, we present a study specific to salinization of a lake.

## Keywords

Solute removal Surrogate model Aquatic environment Risk-based assessment Monte Carlo simulation## Notes

### Acknowledgements

This project was funded by The Jefferson Project at Lake George, which is a collaboration between Rensselaer Polytechnic Institute (RPI), International Business Machines (IBM), and The FUND for Lake George. We gratefully acknowledge the critical reviews by Steven A. Norton and Jeffrey Short on an earlier version of the manuscript.

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