@article{dicarlo_berglund_kaza_grieshop_shealy_behr_2023, title={Customer complaint management and smart technology adoption by community water systems}, volume={80}, ISSN={["1878-4356"]}, DOI={10.1016/j.jup.2022.101465}, abstractNote={Community water systems (CWSs) supply safe drinking water through pipes and other conveyances to the same population year-round. Complaint management is an important activity for CWSs and can assist efforts to monitor water quality and improve public perceptions. This research explores how CWSs receive, store, and use customer complaints. A new dataset is constructed through the distribution of an online survey. Respondents represent more than 500 CWSs across the U.S. and vary in characteristics, including the population size served. This research gives new insight about the tools that CWSs need and are willing to adopt for analyzing and reporting water quality issues.}, journal={UTILITIES POLICY}, author={DiCarlo, Morgan and Berglund, Emily Zechman and Kaza, Nikhil and Grieshop, Andrew and Shealy, Luke and Behr, Adam}, year={2023}, month={Feb} }
 @article{behr_berglund_sciaudone_2022, title={Effectiveness of indicators for assessing the vulnerability of barrier island highways}, volume={105}, ISSN={["1879-2340"]}, DOI={10.1016/j.trd.2022.103234}, abstractNote={Highways along barrier islands are highly susceptible to storm impacts like overwash, erosion, and island breaching. The present research evaluates the effectiveness of 14 morphological indicators in predicting highway vulnerability to storm impacts from a data set of seven storms with documented roadway impacts. Multi-indicator functions were also developed and assessed. The research finds that distance from edge-of-pavement to dune toe, volume above mean high water between edge-of-pavement and ocean shoreline, distance from edge-of-pavement to ocean shoreline, and dune crest height above the road are the most skilled individual indicators of highway vulnerability. A multi-indicator function of dune toe elevation and distance from edge-of-pavement to dune toe is more skilled than any of the individual indicators that were evaluated. Some of these indicators can be projected to assess future vulnerability, as well. The results convey the value of geomorphology-based indicators and their potential in larger-scale coastal infrastructure vulnerability assessments.}, journal={TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT}, author={Behr, Adam and Berglund, Emily and Sciaudone, Elizabeth}, year={2022}, month={Apr} }
 @article{pesantez_behr_sciaudone_2022, title={Importance of Pre-Storm Morphological Factors in Determination of Coastal Highway Vulnerability}, volume={10}, ISSN={["2077-1312"]}, url={https://doi.org/10.3390/jmse10081158}, DOI={10.3390/jmse10081158}, abstractNote={This work considers a database of pre-storm morphological factors and documented impacts along a coastal roadway. Impacts from seven storms, including sand overwash and pavement damage, were documented via aerial photography. Pre-storm topography was examined to parameterize the pre-storm morphological factors likely to control whether stormwater levels and waves impact the road. Two machine learning techniques, K-nearest neighbors (KNN) and ensemble of decision trees (EDT), were employed to identify the most critical pre-storm morphological factors in determining the road vulnerability, expressed as a binary variable to impact storms. Pre-processing analysis was conducted with a correlation analysis of the predictors’ data set and feature selection subroutine for the KNN classifier. The EDTs were built directly from the data set, and feature importance estimates were reported for all storm events. Both classifiers report the distances from roadway edge-of-pavement to the dune toe and ocean as the most important predictors of most storms. For storms approaching from the bayside, the width of the barrier island was the second most important factor. Other factors of importance included elevation of the dune toe, distance from the edge of pavement to the ocean shoreline, shoreline orientation (relative to predominant wave angle), and beach slope. Compared to previously reported optimization techniques, both machine learning methods improved using pre-storm morphological data to classify highway vulnerability based on storm impacts.}, number={8}, journal={JOURNAL OF MARINE SCIENCE AND ENGINEERING}, publisher={MDPI AG}, author={Pesantez, Jorge E. and Behr, Adam and Sciaudone, Elizabeth}, year={2022}, month={Aug} }