Hydrologic Predictions Using Probabilistic Relational Models
Abstract
The US Army faces a significant burden in planning sustainment operations. Currently, logistics planners must manually evaluate potential emplacement sites to determine their terrain suitability. Sites subject to rainfall-runoff responses such as flooding are ill-suited for emplacements, but evaluating the likelihood of such responses requires significant time and expertise. To reduce the time and to ease the difficulty of logistics site selection we demonstrated a series of Terrain Impact Decision Extensions (TIDE) for use in logistics planning tools and processes. TIDE performs data-fusion over a variety of terrain and weather data sets using probabilistic relational models (PRMS), providing a high-performance alternative to physics-based hydrologic models. 1.
Cite
Text
Metzger et al. "Hydrologic Predictions Using Probabilistic Relational Models." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Metzger et al. "Hydrologic Predictions Using Probabilistic Relational Models." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/metzger2014uai-hydrologic/)BibTeX
@inproceedings{metzger2014uai-hydrologic,
title = {{Hydrologic Predictions Using Probabilistic Relational Models}},
author = {Metzger, Max and O'Connor, Alison and Boutt, David},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {31-40},
url = {https://mlanthology.org/uai/2014/metzger2014uai-hydrologic/}
}