Optimizing shoreline suitability models framework: integrating advanced GIS methodologies at Aberdeen Proving Ground, Maryland

Introduction Coastal shorelines are experiencing increased vulnerability from sea-level rise, erosion, and stronger storm activity, highlighting the critical need for adaptive and evidence-based shoreline management approaches that evaluate the site suitability of living shoreline interventions. Traditional hard-armoring solutions, such as seawalls and bulkheads, while protective, often degrade habitats and disrupt natural coastal processes. This study advances a standardized, GIS-based framework for shoreline suitability modeling to support the adoption of living shorelines (LS) and hybrid solutions (HS) as nature-based alternatives. Methods Focusing on Aberdeen Proving Ground (APG), Maryland, a low-lying coastal military installation along the Chesapeake Bay, we used ArcGIS Pro’s Suitability Modeler (SM) to integrate thirteen physical and ecological variables derived from site assessments and prior studies. The model classified shoreline segments as suitable for LS, HS, or not suitable for living shoreline (NLS) using both weighted and unweighted multi-criteria approaches. Results Results indicate that the weighted SM classified 30.4% of the shoreline as suitable for LS and 69.5% as suitable for HS, while the unweighted SM increased LS suitability to 33.5% and identified 66.4% as HS, with <1% categorized as NLS in both cases. Weighting increased HS classification by 3.1%, whereas it decreased LS classification by 3.1%. A three-step validation using a confusion matrix, the Living Shoreline Feasibility Model (LSFM), and sensitivity analysis was performed. The weighted model demonstrated stronger agreement beyond chance (Cohen’s Kappa = 0.71) compared to the unweighted model (0.53), indicating improved classification consistency. Introducing weight also improved alignment with LSFM classifications and enhanced differentiation among suitability categories. Sensitivity analysis indicates that classification outcomes are robust to reasonable variations in weights, supporting confidence in the observed LS/HS pattern. Discussion This study addresses major gaps in prior modeling efforts, specifically the lack of variable standardization, weighting transparency, and multi-step model validation, by offering a transferable, replicable, and data-driven framework. The proposed approach strengthens decision-support capabilities for coastal planners, providing a scientifically robust tool for scaling nature-based shoreline and hybrid protection and advancing coastal resilience.