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Abstract. High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.
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Participatory and Evolutionary Fire Simulation via a Sensitive Control of Key Scenery Parameters
Fire simulator training is a very effective way to reduce firefighter injuries and improve their mission performance. However, the usability of existing fire simulators remains an issue as most of these simulators require users to set up hundreds or even thousands of parameters to initiate the simulation. In this paper, we present a participatory and evolutionary fire simulation method that balances the accuracy and fidelity of the fire simulation and the usability. The base parameters for accurate and usable fire simulation can be selected by a combination of the evolutionary algorithm and the knowledge of domain experts via the virtual reality (VR) fire simulator. These base parameters are expected to expedite the use of fire simulators and reduce the adoption threshold.
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- Award ID(s):
- 1937878
- PAR ID:
- 10152103
- Date Published:
- Journal Name:
- ASCE Computing in Civil Engineering Conference 2019
- Page Range / eLocation ID:
- 103 to 111
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482421.014
