Amir H. Behzadan’s research while affiliated with University of Colorado Boulder and other places

Like all job applicants, veterans have to face the ubiquitous employment interview and pass this potential hurdle to civilian sector employment. So, because of the uniqueness of transitioning from the military to civilian employment, the present paper sought to identify perceived interviewing strengths and weaknesses of veteran interviewees from (a) the perspective of civilian sector human resource professionals (i.e. hiring personnel) with experience interviewing veterans (Study 1, five focus groups, N = 14), and (b) veterans (Study 2, N = 93). Qualitative analysis of the focus group transcripts resulted in the emergence of two theme categories: (1) veteran interviewee strengths and (2) veteran interviewee weaknesses. This information guided the development of a 10-item survey that was completed by 93 veterans (Study 2). In its totality, the results (from both Study 1 and Study 2) indicated that communication of soft skills, confidence, and professionalism were perceived to be strengths that veterans displayed during civilian employment interviews, and conversely, the ineffective translation and communication of relevant technical skills acquired in the military, use of military jargon, and nervousness were considered to be weaknesses. Recommendations to capitalize on the strengths and mitigate the weaknesses are presented.

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens’ response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Successful flood response and evacuation require timely access to reliable flood depth information in urban areas. However, existing flood depth mapping tools do not provide real-time flood depth information in residential areas. In this paper, a deep convolutional neural network is used to determine flood depth through the analysis of crowdsourced images of submerged stop signs. Model performance in pole length estimation is tested on a test set, achieving root mean squared error (RMSE) of 10.200 in. on pre-flood photos and 6.156 in. on post-flood photos, and an average processing time of 0.05 seconds. The performance of the developed model is tested on two case studies: Hurricane Ian in the U.S. (2022), and the Pacific Northwest floods in the U.S. and Canada (2021), yielding MAE of 4.375 in. and 6.978 in., respectively. The overall MAE for both floods was achieved as 5.807 in., which is on par with previous studies. Additionally, detected flood depths are compared with readings reported by the nearest flood gauge on the same date. The outcome of this study demonstrates the applicability of this approach to low-cost, accurate, scalable, and real-time flood risk mapping in most geographical locations, particularly in places where flood gauge reading is not attainable.

About half of the world’s population resides near coastlines, rivers, or inland streams. Climate change has led to more severe hydro-hazards (e.g., floods, storms) in these communities, causing significant economic damage and loss of life. Informed decision-making during flood evacuation, search and rescue, and sheltering depends on the availability of reliable information about the depth of floodwater in affected areas. While underestimating the water depth can be catastrophic, overestimating it may severely delay the deployment of goods and services. Our perception of risk and prior experience with floods influence how we interpret information to arrive at a decision. In this study, we utilize immersive virtual reality (VR) to reconstruct urban flood scenes and conduct a series of user studies to assess the human perception of flood risk on urban roads. This VR prototype is sought to improve human perception and communication of flood risks.