Abstract
In this paper we propose a novel snow accumulation and melt model, formulated as a Dynamic Bayesian Network (DBN). We encode uncertainty explicitly and train the DBN using Monte Carlo analysis, carried out with a deterministic hydrology model under a wide range of plausible parameter configurations. The trained DBN was tested against field observations of snow water equivalents (SWE). The results indicate that our DBN can be used to reason about uncertainty, without doing resampling from the deterministic model. In all brevity, the DBN’s ability to reproduce the mean of the observations was similar to what could be obtained with the deterministic hydrology model, but with a more realistic representation of uncertainty. In addition, even using the DBN uncalibrated gave fairly good results with a correlation of \(0.93\) between the mean of the simulated data and observations. These results indicate that hybrids of classical deterministic hydrology models and DBNs may provide new solutions to estimation of uncertainty in hydrological predictions.
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Matheussen, B.V., Granmo, OC. (2015). Modeling Snow Dynamics Using a Bayesian Network. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_37
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DOI: https://doi.org/10.1007/978-3-319-19066-2_37
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