Zanotti, L., Z. Ma, J. L. Johnson, D. R. Johnson, D. J. Yu, M. Burnham, and C. Carothers. 2020. Sustainability, resilience, adaptation, and transformation: tensions and plural approaches. Ecology and Society 25(3):4. https://doi.org/10.5751/ES-11642-250304

Johnson, D.R.; Fischbach, J.R., and Ortiz, D.S., 2013. Estimating surge-based flood risk with the coastal Louisiana risk assessment model. The Coastal Louisiana Risk Assessment model (CLARA) was designed to facilitate comparisons of current and future flood risk under a variety of protection system configurations in a wide range of environmental, operational, and economic uncertainties. It builds on previous studies of coastal risk by incorporating system fragility and a larger number of future scenarios than previously analyzed. Flood depths and direct economic damage from a wide range of simulated storm events are aggregated to produce a statistical summary of coastal risk under different assumptions about future conditions. CLARA's estimates of project-level effects on flood risk reduction were used as one of the key decision drivers in selecting the risk reduction projects included in the Master Plan. Depending on the scenario, the final alternative is projected to reduce expected annual dama...

Floods are the most common and damaging natural disaster worldwide in terms of both economic losses and human casualties. Currently, policymakers rely on data collected through labor-intensive and, consequently, expensive street-level surveys to assess flood risks. We propose a laborless and financially feasible alternative: a framework that can effectively and efficiently collect building attribute data without manual street surveys. By utilizing deep learning, the proposed framework analyzes Google Street View (GSV) images to estimate multiple attributes of buildings simultaneously—including foundation height, foundation type, building type, and number of stories—that are necessary for assessing flood risks. The proposed framework achieves a 0.177-m mean absolute error (MAE) for foundation height estimation and classification F1 scores of 77.96% for foundation type, 83.12% for building type, and 94.60% for building stories, and requires less than five days to predict the attributes of 0.8 million buildings in coastal Louisiana.

Planners managing coastal flood risk under a constrained computational budget face a tradeoff. Simulating many time periods or scenarios limits how many storm simulations can be run on each landscape. In this analysis, we present a deep learning model to predict storm surge as a function of storm parameters but also landscape features and boundary conditions (e.g., sea level). It is trained on peak surge elevations from Advanced Circulation (ADCIRC) hydrodynamic simulations of coastal Louisiana in a 2020 baseline and decadal periods from 2030 to 2070 under two morphological and climate scenarios. Leave-one-landscape-out cross-validation yielded a 0.086-m RMSE and 0.050-m MAE over 90 storms per landscape and 94,013 geospatial locations. A two-sided Kolmogorov-Smirnov test comparing annual exceedance probability (AEP) estimates from the model predictions to ADCIRC simulations rejected the null hypothesis that the predicted and ADCIRC AEP values were drawn from the same distribution only 1.1% of the time.