Coastal regions worldwide are increasingly vulnerable to a combination of factors, including sea level rise (SLR), intensified wave action, storm surges, erosion, and natural disasters such as stronger storms and hurricanes (NOAA, 2015; Gittman et al., 2016). To protect populations in these vulnerable coastal areas, traditional coastal defense methods, commonly known as armoring, hard armoring, or shoreline hardening, such as revetments, seawalls, bulkheads, breakwaters, ripraps, and pier pilings, have been widely used. Projections by the NOAA indicate that the global population living in coastal areas will continue to grow, potentially doubling the extent of hardened shorelines by 2,100 (NOAA, 2013). While armored structures mitigate wave forces, they disrupt the land-water continuum, destroy critical habitats, and hinder natural sediment accretion. Additionally, these structures exhibit limited long-term resilience, requiring human intervention to adapt to changing environments (Berman and Rudnicky, 2008; Mitsova et al., 2016; Dobbs et al., 2017; Boland et al., 2018; Nunez et al., 2022).

In the United States (US), shoreline hardening is occurring at an alarming rate, with approximately 200 km (about 124 mi) of shoreline being fortified annually. Of the 160,168 km (about 99,524 mi) of tidal shoreline in the continental US, 22,842 km (14%) is already hardened (Gittman et al., 2015). Without effective restrictions, approximately 33% of the contiguous US shorelines may become armored. Currently, states such as, North Carolina, South Carolina, Maine, Virginia, Rhode Island, Massachusetts, Maryland, Oregon, Washington, and California, have implemented significant restrictions on shoreline hardening (Gittman et al., 2015). Despite regulatory restrictions in Maryland, the Virginia Institute of Marine Science (VIMS) Shoreline Inventory indicates that more than 25% of the state’s shoreline, approximately 1,610 km (1,000 miles), has already been hardened, while nearly 18% of the Chesapeake Bay’s shoreline is armored (Berman et al., 2006; Bilkovic et al., 2016; Nunez et al., 2023).

Since the mid-2000s, researchers have explored ways to promote natural and resilient approaches to shoreline stabilization to move away from hardened barriers and structures (Burke et al., 2005; Currin, 2019; NOAA, 2015; Morris et al., 2018). These strategies, collectively known as soft stabilization, are referred to by various names, including non-structural, soft-structural, living shorelines (LS), and nature-based solutions (NBS). Soft stabilization strategies integrate natural materials into coastal edge protection, leveraging plants, sand, or rock along with living resources such as oysters and coral reefs (NOAA, 2023). These solutions offer comparable advantages to traditional hardened structures (Dobbs et al., 2017; Nunez et al., 2022). LS, for instance, combines biodegradable materials, vegetation, and shellfish to accrete sediment and restore habitat. They are most effective in low-energy environments, providing benefits such as improved water quality, enhanced biodiversity, erosion control, aesthetic value, lower costs, and additional recreational opportunities (Rezaie et al., 2020; Currin, 2019; Morris et al., 2018; Dunn, 2010). For moderate to high-energy environments, hybrid solutions (HS) are more suitable. These combine protective structures with living components to dampen wave energy and protect the living shoreline elements. Typically, HS includes a low rock or concrete sill to attenuate waves. When certain materials, like concrete, are used for the sill, they can also serve as habitats for bivalves such as clams, mussels, oysters, and scallops. These hybrid solutions are appropriate for energetic systems, offering greater habitat value than traditional hardened solutions while maintaining the land-water continuum.

To assist decision-makers in transitioning from traditional hardened coastal protection methods to sustainable, nature-based solutions, including living and hybrid approaches, several site suitability models have evolved. However, when we analyzed eight regional and global shoreline suitability models published between 2008 and 2022 on the US Eastern coast (Berman and Rudnicky, 2008; Carey, 2013; Zylberman, 2016; Mitsova et al., 2016; Dobbs et al., 2017; Boland et al., 2018; Wisener, 2018; Nunez et al., 2022), we found a non-standardized approach with considerable methodological inconsistencies in the selection of variables, site assessment, model validation strategies, and appropriate GIS tools. The choice of variables plays a critical role in determining the accuracy of living shoreline (LS) suitability models, as it directly influences the reliability of the results, yet existing models vary widely in the selection of physical, ecological, and social variables. As shown in Table 1, most models incorporate physical variables like bathymetry and fetch, as well as ecological variables such as marsh presence, but significantly vary in the selection of other variables. Also, there is no criterion and prior site assessments observed that can justify and lead to the selection of variables for a specific context. These models also lack weighting schemes and model validation approaches, limiting confidence in their predictive power and limiting the applicability of results for decision-making. Overall, these models lack standardized frameworks for broader applications in shoreline suitability analysis.

This lack of consistency and validation represents a critical barrier for translating suitability models into actionable coastal resilience planning tools. This study addresses these gaps by developing and testing an optimized GIS-based shoreline suitability framework at Aberdeen Proving Ground (APG), Maryland, a coastal military installation highly exposed to SLR and wetland loss. The framework makes four contributions to existing knowledge:

Integration of advanced GIS methodologies: Using ArcGIS Pro’s Suitability Modeler (SM), the new site suitability analysis tool released by ESRI in 2021, we evaluate both weighted and unweighted multi-criteria approaches, enabling a systematic comparison of how weighting influences model outputs.

Three-step validation: No study has compared model outputs against independent tools (e.g., Living Shoreline Feasibility Model), and only two have been validated based on the ground conditions (Berman and Rudnicky, 2008; Nunez et al., 2022), limiting confidence in their predictive power. We validate results against the Living Shoreline Feasibility Model (LSFM), developed by the Partnership for the Delaware Estuary (Partnership for the Delaware Estuary, 2022), and provide guidance on determining the feasibility of LS for specific sites, and through a confusion matrix and sensitivity analysis, demonstrating a replicable strategy for enhancing model reliability.

Thorough site assessment and case-specific variable selection: This study explicitly integrates physical (e.g., bathymetry, fetch, slope) and ecological (e.g., vegetation type, marsh presence, habitat continuity) variables, excluding social variables to justify the site-specific needs. This structured variable categorization ensures the model’s adaptability to diverse coastal environments with varying geomorphological and socio-ecological contexts.

Framework adaptability: By explicitly selecting physical and ecological variables based on thorough site assessment and prior model evaluation, the framework is designed for transferability to other coastal regions, bridging the gap between site-specific studies and scalable shoreline resilience planning. By providing transparent criteria and standardized workflows within ArcGIS Pro, the framework enables replication and scaling to other coastal regions, thereby bridging the gap between localized suitability analyses and broader, regional shoreline resilience planning.

The overarching goal of this research is to advance the precision, transparency, and applicability of shoreline suitability models for nature-based and hybrid shoreline solutions for coastal protection. By refining model inputs, testing weighting schemes, and incorporating multi-step validation, this study contributes both to coastal resilience science and to the GIScience literature on spatial suitability modeling. Ultimately, the work provides decision-makers with a more reliable and adaptable tool for prioritizing NBS, reducing reliance on hardened structures, and enhancing coastal ecosystem resilience.

To develop a spatialized shoreline suitability model, this study focused on APG, Maryland, using the SM tool in ArcGIS Pro 3.6.0. APG, located along the northern Chesapeake Bay, is a low-lying coastal military proving ground threatened by rising seas and wetland loss. Figure 1 illustrates the methodology framework, including data acquisition, variable selection, model execution, and validation. Shoreline segments were classified into three categories: suitable for living shoreline (LS), suitable for hybrid solutions (HS), and not suitable for living shoreline (NLS). The adaptable framework developed in this study can be applied to other coastal regions, providing a valuable tool for assessing shoreline resilience and identifying effective nature-based solutions.

This study evaluated a GIS-based shoreline suitability modeling framework for identifying locations appropriate for LS and HS at APG, Maryland, with an emphasis on the influence of variable weighting, model performance using a new GIS tool, and methodological transparency through a three-step validation framework. By applying the SM in ArcGIS Pro and integrating site-specific physical and ecological variables, the study demonstrates how weighting schemes, validation procedures, and data availability shape shoreline classification outcomes.

This study contributes broadly to shoreline suitability modeling and GIS-based decision support beyond the APG context, demonstrating how the framework can be adapted to other coastal systems by substituting regional datasets and following a standardized approach. The focus is on methodological generalizability, including data processing, suitability criteria, weighting structure, and model comparison workflow, demonstrated through a challenging site, APG, which is an active military testing facility facing critical environmental challenges, including SLR, shoreline erosion, and the presence of critical habitat. Methodological choices substantially influence model outcomes, as prior studies show considerable variation in variable selection, weighting schemes, and validation methods, often without a consistent or transparent framework, thereby limiting the reliability and comparability of model inputs, formulations, and outputs (Table 1).

Applying the standardized approach to APG, this study selected physical and ecological variables informed by prior literature, site-specific assessments, and a regional decision-support tool (LSFM). Given the site’s unique context, which lacks residential communities or adjacent civilian infrastructure, social variables were intentionally excluded to maintain alignment with the environmental and operational characteristics of APG. Model performance was evaluated using a three-step validation framework comprising a confusion matrix, comparison with LSFM outputs, and sensitivity analysis. Together, these methodological advances address key gaps in existing shoreline suitability modeling practices and offer a more adaptable, transparent, and transferable framework for coastal resilience assessments across diverse site conditions.

The comparison of weighted and unweighted suitability model outputs highlights the influence of variable weighting on shoreline classification outcomes. Applying weights altered the proportional allocation of suitability classes along the APG shoreline. The weighted model classified 30.4% of the shoreline as suitable for LS and 69.5% for HS, whereas the unweighted model identified 33.5% LS and 66.4% HS. This shift reflects the importance of variables such as fetch, wave hazard, storm surge, coastal structures, and shoreline sensitivity, factors that strongly influence the feasibility of nature-based interventions in high-energy environments like APG and are consistent with expert recommendations for unequal weight assignment in MCDA analyses (Eastman, 1999; Kiker et al., 2005; Malczewski, 2004; Moffatt, 2008; Chen et al., 2013).

Model performance metrics further support the use of weighting. The weighted model achieved a higher Cohen’s Kappa (0.71) compared to the unweighted model (0.53), indicating stronger agreement beyond chance and greater overall classification consistency. Although weighting introduces class-specific tradeoffs in precision and accuracy, it emphasizes a larger proportion of shoreline segments as suitable for HS rather than LS, reinforcing the characterization of the APG coastline as predominantly high-energy. Validation using the LSFM demonstrated substantially higher agreement for the weighted model (75%) relative to the unweighted model (25%). While this agreement reflects calibration rather than fully independent validation, given that LSFM-derived insights informed the weighting scheme, it nevertheless supports the physical realism and decision relevance of the weighted approach.

Weighting also produced meaningful spatial shifts by amplifying the influence of five key variables, resulting in a 3.1% increase in HS classification and a corresponding 3.1% decrease in LS classification. For APG management, these shifts are consequential, as they help differentiate locations where HS are more appropriate than LS, thereby supporting more risk-informed, site-specific shoreline planning decisions. Across all ten sensitivity scenarios, limited variation in outputs indicates that the model is not highly sensitive to minor weight adjustments, suggesting robustness to reasonable uncertainty in variable weighting.

The APG site presents a complex test case due to high-energy conditions, the absence of residential shorelines, the presence of critical ecological resources, and widespread exposure to storm surge, SLR, and erosion. These conditions explain why most shoreline segments were classified as suitable for HS rather than LS in the weighted scenario. Despite this, the region still contains ecologically valuable low-energy pockets with shallow bathymetry, low fetch, and existing marsh vegetation characteristics that make LS feasible and beneficial in select areas. These spatial distinctions are particularly important for APG coastal planners considering nature-based alternatives to hardened infrastructure, especially given accelerating SLR projections for Maryland and the Chesapeake Bay.

A key contribution of this study is the development of a structured, replicable GIS framework that combines site assessment, transparent variable selection, variable weighting, and multi-stage validation. Prior shoreline suitability models have often lacked methodological consistency in weighting schemes, validation strategies, and variable justification. By integrating LSFM guidance, thorough site assessment, and standardized GIS workflows, this study responds to calls for greater transparency and comparability across coastal modeling efforts. Although social variables were intentionally excluded due to the unique land-use context of APG, the framework remains flexible, allowing their integration in regions where community infrastructure, access, or policy constraints influence shoreline feasibility.

Despite these contributions, several limitations should be acknowledged. First, data gaps and coarse-resolution inputs constrained analysis in inner channels of APG, where areas lacking reliable data were intentionally excluded from the analysis rather than classified as suitable or unsuitable. While this approach avoids artificially biasing suitability results, it reduces spatial coverage and limits interpretability in these environments. Future work should incorporate higher-resolution datasets, targeted field validation, or updated remote-sensing products to strengthen classification confidence. Second, the study relied on static datasets and did not model temporal dynamics such as seasonal marsh changes, sediment movement, or storm-driven shoreline shifts. Incorporating time-series analyses or forecast scenarios could better capture evolving coastal conditions. Third, validation relied on input-layer comparisons and LSFM-based scoring rather than independent LS/HS/NLS ground truth data. Future studies should prioritize field-based evidence or UAV-derived shoreline surveys to improve validation robustness. Fourth, while ground surveys were not included, they are recommended prior to implementing any LS or HS intervention to account for site-specific environmental, anthropogenic, and operational conditions. In particular, planning interventions at “Spesutie Island”, a vulnerable, low-lying segment of APG, will require careful analysis, as the entire coastline may be submerged under 1 ft of SLR, and the sole access bridge can be affected even by a Category one storm.

Overall, the results demonstrate that weighted, GIS-based multi-criteria models can effectively differentiate shoreline segments for LS and HS suitability when informed by expert-driven criteria and contextual site knowledge. The approach strengthens decision-support capacity for coastal planners by providing a transparent, adaptable modeling structure transferable to other coastal settings with appropriate substitution of local datasets and policy considerations. For APG and similar high-energy environments, the findings underscore the practicality of prioritizing HS while strategically identifying low-energy areas where LS can deliver ecological and protective benefits. As coastal regions face increasing pressures from SLR, erosion, and habitat degradation, such standardized yet flexible modeling frameworks are essential for advancing resilient, nature-based shoreline management.

This study presents a site-specific yet transferable GIS-based shoreline suitability modeling framework for classifying LS and HS applicability at APG, Maryland. By implementing the SM tool in ArcGIS Pro and systematically evaluating variable weighting, model comparison, and multi-step validation strategies, the research demonstrates how methodological choices influence shoreline classification outcomes, particularly in high-energy coastal environments.

A key contribution of this study is the development of a transparent and transferable modeling framework that integrates literature-informed variable selection, expert-guided weighting, standardized GIS workflows, and multi-stage validation. While applied to the unique operational and environmental context of APG, the framework is readily adaptable to other coastal systems through the substitution of regional datasets and locally relevant criteria.

Results indicate that weighting improves model consistency and agreement, classifying approximately 30% of the APG shoreline as suitable for LS and about 70% for HS, reflecting dominant high-energy conditions. The weighted model achieved stronger agreement beyond chance (Cohen’s Kappa = 0.71) than the unweighted model (0.53) and demonstrated substantially greater alignment with the Living Shorelines Feasibility Model (75% versus 25%). The sensitivity analysis further demonstrates that the weighted suitability model is stable and reliable, with outputs showing only minor changes under ±1 variations in the weights of critical variables. This reinforces confidence that the classification results, particularly the ∼30% LS and ∼70% HS pattern, are not artifacts of subjective weighting but reflect underlying shoreline conditions at APG.

Although inner-channel areas lacking reliable data were excluded from the analysis, and temporal shoreline dynamics were not modeled, the results provide a strong foundation for prioritizing shoreline strategies at APG where appropriate. Future work should incorporate higher-resolution datasets, time-series analyses, and field-based validation to further strengthen model performance.

Overall, this research demonstrates that weighted, GIS-based multi-criteria models offer a practical and adaptable decision-support tool for shoreline planning. For APG and similar high-energy coastlines, the findings support prioritizing HS while strategically identifying low-energy segments where LS can deliver ecological and protective benefits. More broadly, the framework provides a transferable approach for integrating resilience, adaptation, and nature-based solutions into coastal management under accelerating SLR and erosion pressures.