edu.ucar.opensky::articles:23059 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:20:50.400131 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7g44tgk Quantifying uncertainty in source term estimation with tensorflow probability 2019-12-01T00:00:00Z publication https://n2t.org/ark:/85065/d7g44tgk https://doi.org/10.1109/UrgentHPC49580.2019.00006 Fanfarillo, Alessandro author UCAR/NCAR - Library publisher Fast and accurate location and quantification of a dangerous chemical, biological or radiological release plays a significant role in evaluating emergency situations and their consequences. Thanks to the advent of Deep Learning frameworks (e.g. Tensorflow) and new specialized hardware (e.g. Tensor Cores), the excellent fitting ability of Artificial Neural Networks (ANN) has been used by several researchers to model atmospheric dispersion. Despite the high accuracy and fast prediction, regular ANNs do not provide any information about the uncertainty of the prediction. Such uncertainty can be the result of a combination of measurement noise and model architecture. In an urgent decision making situation, the ability to provide fast prediction along with a quantification of the uncertainty is of paramount importance. In this work, a Probabilistic Deep Learning model for source term estimation is presented, using the Tensorflow Probability framework. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2019 IEEE. None eng geoscientificInformation