Wildfire Safe Egress (WiSE) integrates the fire dynamics, human behavior, and traffic model to predict the chance of safe egress by any given community during a wildfire evacuation. WISE framework presents a unified dependency diagram and workflow offering consistent granularity between sub-models and creates comparable evacuation scenarios. A human behavior model is used to predict the community decision making and action based on their socio-demographic vulnerability profile. An agent-based stochastic approach generates evacuation departure times. The travel times are calculated through a congestion-informed traffic simulation, and a Bayesian Network is used to combine the sub-models and to predict community safety (probability of successful evacuation) via probabilistic inference based on the integrated model.

It is an open access and web-based graphical programming environment developed at UCLA for preprocessing multi-sensor monitoring data and developing deep learning solutions in general, and for reliability and maintenance areas in particular. In DataBruin, implementation of the data flow diagrams in an intuitive drag-and-drop manner not only offers a fast prototyping and code-free platform by helping practitioners to focus on the concepts rather than the syntax but also makes the opportunity to guide them toward error-free prototyping of deep learning-based Prognostics and Health Management (PHM) models.  Also, DataBruin provides standardization on the structure of deep learning PHM projects by offering a comprehensive path from preparing datasets all through the predictions and necessary assessments, besides the standard auto-generated Python code for each analysis step.