Sustainable Selection of Disaster Recovery Centers: A Comparative GIS Analysis and Fucom-Based Electre I Approach for Digital Infrastructure Resilience

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study focuses on the sustainable site selection of Disaster Recovery Centers (DRCs) and proposes an integrated decision-making framework that combines GIS-based spatial suitability analysis with the Fuzzy FUCOM-weighted ELECTRE I outranking model. Taking Sinop University in Turkey as the research object, the study evaluates five potential districts in Sinop province, aiming to address the multidimensional needs of digital infrastructure resilience and organizational business continuity. Generally, the manuscript selects a meaningful and practical research topic, with a rigorous overall research design and detailed methodological implementation. The English expression is clear and fluent, with only minor formatting or typographical issues that require revision. The research has certain theoretical and application value and potential for publication. However, there are still some deficiencies that need to be improved and supplemented:

1. Introduction: At the end of the literature review in the introduction section, a paragraph should be added to systematically sort out the current application status of Multi-Criteria Decision Making (MCDM) methods in the field of Disaster Recovery Center (DRC) site selection, clarify the deficiencies of existing research in terms of site selection dimensions and method selection, and further highlight the research gap and necessity of this study.
2. The specific basis for selecting 5 regions in Sinop Province, Turkey as alternative objects for DRC site selection should be supplemented. Combined with the disaster risk characteristics, geographical conditions, and correlation with Sinop University of each region, explain their representativeness and rationality.
3. Detailed explanations should be added to the 8 evaluation criteria (C1-C8) in Section 3.3, including the quantitative calculation method, data source, and acquisition path of each criterion. At the same time, the construction and screening process of the indicator system should be elaborated, explaining the basis for building the initial indicator pool and the reasons for finally retaining 8 indicators.
4. In the GIS-based spatial analysis in Section 3.2, it is necessary to clearly state whether there are neutral indicators (i.e., indicators with unclear advantages and disadvantages) in the research. If they exist, specific processing methods should be supplemented; if not, clear evidence should be provided. At the same time, the rationality and basis for adopting equal weights in GIS analysis should be explained.
5. The reasons for selecting the Fuzzy FUCOM method to determine indicator weights should be supplemented. Compare its advantages with commonly used weight determination methods such as AHP, Entropy Weight Method, and CRITIC, and explain why this method is suitable for the research scenario of this paper.
6. The reasons for selecting the ELECTRE I model as the core decision-making model should be supplemented. Compare its differences with compensatory decision-making models such as TOPSIS and VIKOR and explain the adaptability and advantages of this non-compensatory model in DRC site selection decisions.
7. In the weight result analysis in Section 4.1, detailed information on expert consultation should be supplemented, including the professional background, years of experience, and quantity of experts, the distribution and recovery of expert questionnaires, the recovery rate, as well as the integration method and consistency test process of expert opinions.
8. The conclusion section needs to reorganize the structure, focusing on highlighting the academic contributions and methodological innovations of this paper, supplementing the limitations and deficiencies of the research, and integrating and summarizing in combination with core numerical results (such as the weight of each indicator and the ranking result of alternative regions) to avoid disconnection between the conclusion and the results.
9. The policy implication section should be closely combined with the core research results of this paper, and put forward specific and implementable suggestions for universities and local governments based on the site selection conclusions of the 5 alternative regions in Sinop Province and the weight importance of each indicator, so as to strengthen the practical guiding value of the research.
10. The reference section should supplement high-quality literature related to disaster recovery center site selection, Fuzzy FUCOM-ELECTRE I method application, and digital infrastructure resilience in the past 3-5 years. In the MCDA model, there is also a Combination Weighting of Game Theory method (CWGT) that can be adopted. At the same time, standardize the format of references to ensure the authority and relevance of literature.

Author Response

Comment 1: Introduction: At the end of the literature review in the introduction section, a paragraph should be added to systematically sort out the current application status of Multi-Criteria Decision Making (MCDM) methods in the field of Disaster Recovery Center (DRC) site selection, clarify the deficiencies of existing research in terms of site selection dimensions and method selection, and further highlight the research gap and necessity of this study.

Response: A new paragraph has been added at the end of the Introduction to systematically classify existing MCDM approaches used in disaster-related facility location problems into three main methodological groups. Furthermore, the limitations of current studies—particularly the dominance of compensatory decision-making methods and the limited consideration of digital infrastructure continuity—have been explicitly discussed. Based on this analysis, the research gap has been clearly identified, and the necessity of integrating GIS-based spatial analysis with non-compensatory decision logic has been emphasized.

Comments 2: The specific basis for selecting 5 regions in Sinop Province, Turkey as alternative objects for DRC site selection should be supplemented. Combined with the disaster risk characteristics, geographical conditions, and correlation with Sinop University of each region, explain their representativeness and rationality.

Response: 

The selection of the five districts has been clarified in Section 3.1 (Study Area). Their representativeness in terms of spatial diversity, disaster risks, and geographical conditions has been emphasized. In addition, their relevance to Sinop University has been highlighted, as they host academic and administrative units and provide suitable conditions for accessibility and infrastructure continuity. Overall, the rationale behind the selection has been clearly strengthened.

Comment 3: Detailed explanations should be added to the 8 evaluation criteria (C1-C8) in Section 3.3, including the quantitative calculation method, data source, and acquisition path of each criterion. At the same time, the construction and screening process of the indicator system should be elaborated, explaining the basis for building the initial indicator pool and the reasons for finally retaining 8 indicators.

Response: 

Section 3.3 has been substantially revised to provide a clearer and more detailed explanation of the evaluation criteria and their development process. Specifically, the construction of the indicator system has been elaborated by introducing a two-stage screening process. An initial set of candidate indicators was identified based on the relevant literature on disaster risk assessment and infrastructure resilience, as well as ISO/IEC 27000 series standards and national digital transformation guidelines. These indicators were then structured under four main dimensions and screened according to their relevance to DRC requirements, data availability, and applicability to the study area, resulting in the final set of eight criteria.

Furthermore, detailed information regarding the calculation methods, data sources, and data acquisition procedures for each criterion has been added and is presented in Table 2. In addition, it has been clarified that all criteria were evaluated by experts within the FUCOM framework, while selected criteria were also quantified using GIS-based spatial analysis. The distinction between quantitative (GIS-based) and qualitative (expert-based) evaluation approaches has been explicitly stated. Moreover, the GIS-based spatial analysis and the FUCOM–ELECTRE decision-making process were conducted independently, and their results were comparatively analyzed to highlight methodological differences and provide a more comprehensive evaluation.

Comment 4: In the GIS-based spatial analysis in Section 3.2, it is necessary to clearly state whether there are neutral indicators (i.e., indicators with unclear advantages and disadvantages) in the research. If they exist, specific processing methods should be supplemented; if not, clear evidence should be provided. At the same time, the rationality and basis for adopting equal weights in GIS analysis should be explained.

Response: 

Section 3.2 has been revised to clarify that all criteria used in the GIS-based analysis were defined as monotonic indicators with clear and unidirectional effects on suitability. Accordingly, each criterion was classified as either a benefit or a cost factor based on established environmental and technical considerations. Since no criteria with ambiguous or neutral effects were identified, standard normalization procedures were applied.

In addition, the rationale for adopting equal weights has been further clarified. Equal weighting was intentionally used to provide an objective and data-driven assessment of spatial suitability, independent of expert judgment. This approach ensures that the GIS analysis reflects physical and environmental conditions without introducing subjective prioritization.

In contrast, in the FUCOM–ELECTRE framework, not only operational and infrastructural criteria but also physical criteria were included in the expert evaluation process and weighted accordingly. This allows the same set of criteria to be assessed from a decision-oriented perspective, reflecting expert-based priorities.

Comment 5: The reasons for selecting the Fuzzy FUCOM method to determine indicator weights should be supplemented. Compare its advantages with commonly used weight determination methods such as AHP, Entropy Weight Method, and CRITIC, and explain why this method is suitable for the research scenario of this paper.

Response: 

The rationale for selecting the Fuzzy FUCOM method has been clarified in the manuscript. FUCOM method requires fewer pairwise comparisons and ensures higher consistency compared to AHP, and, unlike objective methods such as Entropy and CRITIC, it allows the integration of expert judgment.

In addition, the advantage of the fuzzy structure in handling uncertainty in expert evaluations has been explained. Considering the expert-driven and multi-dimensional nature of DRC site selection, FUCOM was considered an appropriate and efficient method for this study.

Comment 6: The reasons for selecting the ELECTRE I model as the core decision-making model should be supplemented. Compare its differences with compensatory decision-making models such as TOPSIS and VIKOR and explain the adaptability and advantages of this non-compensatory model in DRC site selection decisions.

Response: 

The rationale for selecting the ELECTRE I method has been clarified in the manuscript, and a brief comparison with compensatory methods such as TOPSIS and VIKOR has been added. It has been emphasized that ELECTRE I adopts a non-compensatory structure, preventing poor performance in critical criteria from being offset by better performance in others. Considering that certain criteria in DRC site selection are non-compensatory in nature, ELECTRE I was considered an appropriate method for this study.

Comment 7: In the weight result analysis in Section 4.1, detailed information on expert consultation should be supplemented, including the professional background, years of experience, and quantity of experts, the distribution and recovery of expert questionnaires, the recovery rate, as well as the integration method and consistency test process of expert opinions.

Response: 

Additional details regarding the expert evaluation process have been added to Section 4.1. The number of experts, their professional backgrounds, and experience levels have been clarified. It has also been specified that all experts were provided with an identical set of criteria and were asked to independently evaluate and rank them according to their relative importance within the FUCOM framework. Furthermore, the aggregation of expert judgments and the consistency of the evaluations have been described in accordance with the requirements of the FUCOM methodology.

Comment 8: The conclusion section needs to reorganize the structure, focusing on highlighting the academic contributions and methodological innovations of this paper, supplementing the limitations and deficiencies of the research, and integrating and summarizing in combination with core numerical results (such as the weight of each indicator and the ranking result of alternative regions) to avoid disconnection between the conclusion and the results.

Response: 

The conclusion section has been reorganized to clearly highlight the academic contribution and methodological framework of the study. Key numerical results, including criterion weights and the ranking of alternative regions, have been explicitly incorporated to ensure consistency with the results section. In addition, the limitations of the study have been summarized to provide a more balanced and comprehensive conclusion.

Comment 9: The policy implication section should be closely combined with the core research results of this paper, and put forward specific and implementable suggestions for universities and local governments based on the site selection conclusions of the 5 alternative regions in Sinop Province and the weight importance of each indicator, so as to strengthen the practical guiding value of the research.

Response: 

The policy implications have been incorporated into the conclusion section to better reflect the core findings of the study. Specific and practical recommendations have been developed based on the comparative results of GIS and ELECTRE analyses (Table 22), including the ranking of alternative regions and the relative importance of the evaluation criteria.

Comment 10: The reference section should supplement high-quality literature related to disaster recovery center site selection, Fuzzy FUCOM-ELECTRE I method application, and digital infrastructure resilience in the past 3-5 years. In the MCDA model, there is also a Combination Weighting of Game Theory method (CWGT) that can be adopted. At the same time, standardize the format of references to ensure the authority and relevance of literature.

Response: 

In response, recent high-quality studies (2023–2025) related to digital infrastructure resilience and infrastructure-based decision-making have been added to the manuscript to strengthen the literature background. In particular, the role of digital infrastructure in enhancing resilience and supporting continuity has been further elaborated.

In addition, hybrid weighting approaches have been discussed, and the Combination Weighting of Game Theory (CWGT) method has been introduced as an alternative approach. However, considering the expert-driven nature of the DRC site selection problem and the limited availability of objective data, the Fuzzy FUCOM method was preferred.

Furthermore, the reference list has been carefully reviewed and standardized to ensure consistency and relevance. All revisions have been incorporated into the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a framework for selecting Disaster Recovery Center (DRC) sites. The paper includes an examination of GIS-based spatial suitability analysis and the ELECTRE I outranking model comparisons, as well as utilizing fuzzy FUCOM weighting to assess operational continuity criteria, in addition to physical risk factors, to create a scalable approach to planning for sustainable digital infrastructure.

This study is well done and informative in its combining multiple techniques (GIS, ELECTRE I, Fuzzy FUCOM) and the explanations of how each applies to the project. The criteria upon which the study is based are both physical suitability and operational resiliency. In addition, operational resiliency includes such things as energy redundancy, continuity of telecommunications systems, and RTO/RPO compliance.

Here are some minor comments to the authors.

1. The abstract describes the methodology and results well. But it should be improved by providing a conclusion or a “take-home message.”

2. In the Materials and Methods section, specifically regarding Fig. 3 and line 316, please clearly explain what the sustainability index is and how it is being calculated. If the index is calculated as explained in line 313, more clarification is required regarding the standardized raster values that the authors used. Please note that when we talk about sustainability, there is a need to be specific about what sustainability is meant to be. It might be a good idea for the authors to highlight which Sustainable Development Goal their work can engage with.

3. In the abstract (Lines 31-32), the authors claim scalability of the proposed framework. However, no other places in the text can we find any further information to support this claim. The authors should try to highlight practical implications. For example, how cities or organizations might use this framework.

Author Response

Comments 1:  The abstract describes the methodology and results well. But it should be improved by providing a conclusion or a “take-home message.”

Response: 

The abstract has been revised to include a clear take-home message. A concluding sentence has been added to highlight the practical contribution of the integrated GIS–MCDM approach in supporting digital resilience and the continuity of critical infrastructures.

Comment 2: In the Materials and Methods section, specifically regarding Fig. 3 and line 316, please clearly explain what the sustainability index is and how it is being calculated. If the index is calculated as explained in line 313, more clarification is required regarding the standardized raster values that the authors used. Please note that when we talk about sustainability, there is a need to be specific about what sustainability is meant to be. It might be a good idea for the authors to highlight which Sustainable Development Goal their work can engage with.

REsponse: 

The explanation of the sustainability index has been clarified and revised in the manuscript. Specifically, the term “sustainability index” has been reconsidered and more precisely defined as a spatial suitability index derived from GIS-based analysis.

The index is calculated using a weighted linear combination approach (Weighted Sum method), where standardized raster layers are integrated following Min–Max normalization. Additional clarification has been provided regarding the standardization process, explicitly stating that all raster values were transformed into a common scale (0–1), where higher values indicate higher suitability and lower risk. Furthermore, inverse normalization procedures (e.g., for slope) have been clearly explained.

In addition, the conceptual meaning of sustainability in this study has been clarified. Rather than representing a composite sustainability index, the proposed approach supports sustainable planning by identifying locations that enhance infrastructure resilience and minimize spatial risk.

Finally, the relevance of the study to the United Nations Sustainable Development Goals has been explicitly stated, particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Comment 3:  In the abstract (Lines 31-32), the authors claim scalability of the proposed framework. However, no other places in the text can we find any further information to support this claim. The authors should try to highlight practical implications. For example, how cities or organizations might use this framework.

REsponse: 

The practical implications of the proposed framework have been clarified in Section 4.6 (Policy Implications and Model Scalability), where its application for local authorities and institutions is explained. In addition, the scalability and adaptability of the model for different regions and institutional contexts have been explicitly described.

Reviewer 3 Report

Comments and Suggestions for Authors

This paper covers a wealth of content from a methodological perspective, yet there are considerable shortcomings in its writing and presentation.
(1) The content on existing research findings in Lines 100–107 of the Introduction is highly important, but the current description is overly vague. It is recommended that the authors focus the Introduction primarily on this relevant content and supplement it with more related literature—at least doubling the number of citations.
(2) In Paragraphs 2–9 of the Literature Review, merely listing relevant studies is ineffective. The authors should compare the methodologies, results, similarities and differences of different studies while summarizing the research ideas.
(3) The review of location selection studies related to digital infrastructure in the Literature Review is brief and fails to systematically summarize the shortcomings and research gaps of existing studies. It is recommended to enhance the targeted nature of the literature review and highlight the value of this study in filling the identified research gaps.
(4) What is the rationale for selecting the study area?
(5) For Figure 1 (the map), basic geographic coordinate information (latitude and longitude) is missing.
(6) In the GIS spatial analysis, equal weights were assigned to all criteria, but the rationality of this equal weighting was not explained. Although this contrasts with the weighting via the Fuzzy FUCOM method used later in the study, the impact of equal weighting on the GIS analysis results was not analyzed. Supplementary explanation on this is recommended.
(7) In spatial data processing, the datasets used vary in resolution and time span. The paper only mentions "standardization and harmonization processing" and notes that the spatial heterogeneity of some data is not obvious, but it does not specify the detailed processing methods or present the results of accuracy verification. The authors are advised to add details of data processing and an analysis of data accuracy.
(8) In the ELECTRE I method, a 1–10 scoring scale was used for the decision matrix, but the basis and criteria for this scoring were not stated, leaving the subjectivity of expert scoring unconstrained. It is recommended to supplement the scoring rules and quantitative standards.
(9) The study results do not include an analysis of the contribution of each criterion to the location selection outcomes, making it impossible to identify the key influencing criteria. A quantitative analysis of criterion contribution is recommended.
(10) I infer that the study is quite extensive and the authors have split it into two papers; however, this leaves the reader with an incomplete understanding. For example, after the comparison of the two methods in Section 4.3, at least a consolidated result should be presented to the reader. Considering that a second paper may be forthcoming, the evaluation results in this paper can be brief, but they should not be omitted entirely.
(11) The study results do not show specific candidate locations within the alternative areas—only analyses at the district/county level were conducted, which limits the practical application value of the research. It is recommended to supplement the analysis with the screening of specific candidate sites within each district/county.
(12) The Discussion section is overly brief. As a methodological paper, there is much to elaborate on—including an evaluation of the proposed method, its advantages, a comparison with other methods, and its potential for wide application—all of which would require reference to more relevant studies. However, the current version lacks in-depth discussion on these aspects.
(13) The study results are not linked to actual urban planning or disaster prevention planning, and the guiding significance of the results for the practical planning of Sinop Province is not explained. It is recommended to supplement the analysis and suggestions on aligning the study results with practical planning.
(14) The proposed framework is claimed to be "scalable and replicable", yet the study only uses 5 districts/counties in Sinop Province as a case study and does not mention how the model can be adapted and adjusted for different regions or types of institutions. Supplementary explanations on the extended application of the model are recommended.
(15) The study limitations only mention issues related to data, the number of experts, and methodology, but neglect the limitations of the criterion system. It is recommended to add directions for improving the criterion system.
(16) Future research recommendations do not mention the intelligent optimization of the model, such as integrating machine learning, big data and other technologies to refine the location selection model. Relevant suggestions for such future research are recommended.

Author Response

Comments (1) The content on existing research findings in Lines 100–107 of the Introduction is highly important, but the current description is overly vague. It is recommended that the authors focus the Introduction primarily on this relevant content and supplement it with more related literature—at least doubling the number of citations.

Response: 

The Introduction has been substantially revised to improve clarity and analytical depth. The literature review component has been expanded with additional recent and relevant studies, significantly increasing the number of references. Furthermore, the relevant section has been restructured to provide a more systematic and analytical overview of existing research, including a clearer classification of MCDM approaches into AHP-based, compensatory, and hybrid approaches, along with a more explicit discussion of their limitations. In addition, the research gap has been more clearly articulated, particularly with regard to the limited consideration of digital infrastructure continuity and the underutilization of non-compensatory decision-making methods.

Comment 2: (2) In Paragraphs 2–9 of the Literature Review, merely listing relevant studies is ineffective. The authors should compare the methodologies, results, similarities and differences of different studies while summarizing the research ideas.

Response: 

The Literature Review section has been thoroughly revised to provide a more analytical and comparative structure, rather than merely listing previous studies. In the revised version, the reviewed studies have been systematically grouped according to their methodological approaches, including weighting techniques (e.g., AHP, BWM, SWARA, Entropy, CRITIC), compensatory ranking methods (e.g., TOPSIS, VIKOR, PROMETHEE), and hybrid approaches (e.g., DEMATEL and ANP).

Within each group, the studies are evaluated comparatively in terms of their methodologies, application contexts, and decision-making characteristics. In particular, similarities and differences among the approaches have been explicitly discussed, including their handling of trade-offs, uncertainty, and decision outcomes. In addition, concise evaluation paragraphs have been included after each methodological group to highlight their respective strengths and limitations.

These analyses demonstrate that most existing studies rely on compensatory decision-making structures, which may allow poor performance in critical criteria to be overlooked.

Comments 3: (3) The review of location selection studies related to digital infrastructure in the Literature Review is brief and fails to systematically summarize the shortcomings and research gaps of existing studies. It is recommended to enhance the targeted nature of the literature review and highlight the value of this study in filling the identified research gaps.

Response: 

The Literature Review section has been substantially revised to provide a more targeted, analytical, and systematic discussion of existing studies, with particular emphasis on digital infrastructure and information system continuity.

In the revised version, the literature has been reorganized to distinguish between studies focusing on physical infrastructure (e.g., emergency logistics, shelter areas, and healthcare facilities) and those addressing digital infrastructure planning. This distinction highlights that existing research predominantly concentrates on physical facility location problems, while the spatial planning of digital infrastructures, such as Disaster Recovery Centers (DRCs), remains relatively underexplored.

Furthermore, the shortcomings of existing approaches have been systematically identified. Specifically, three key limitations have been emphasized: (i) the dominance of compensatory decision-making methods, (ii) the limited consideration of digital infrastructure continuity, and (iii) the insufficient application of non-compensatory approaches in high-risk infrastructure planning.

Based on this analysis, the contribution of the present study has been more clearly positioned as a non-compensatory decision-support framework that comparatively evaluates GIS-based spatial analysis and MCDM approaches, addressing both spatial suitability and digital infrastructure continuity.

Comment 4: What is the rationale for selecting the study area?

Response: 

The rationale for selecting the study area has been further clarified in Section 3.1 (Study Area). The selected districts were determined to reflect the spatial heterogeneity of Sinop Province by considering differences in disaster risk characteristics, geographical conditions, and accessibility levels. In addition, their relevance to Sinop University has been explicitly emphasized, as these districts host academic and administrative units and provide suitable conditions in terms of accessibility, service coverage, and infrastructure continuity. These revisions strengthen both the representativeness and the methodological justification of the study area selection.

Comment (5) For Figure 1 (the map), basic geographic coordinate information (latitude and longitude) is missing.

Response: 

To improve the clarity and interpretability of the map, latitude and longitude coordinates and spatial reference information have been added to Figure 1. The figure has been updated accordingly.

Comment (6) In the GIS spatial analysis, equal weights were assigned to all criteria, but the rationality of this equal weighting was not explained. Although this contrasts with the weighting via the Fuzzy FUCOM method used later in the study, the impact of equal weighting on the GIS analysis results was not analyzed. Supplementary explanation on this is recommended.

Response: 

The rationale for adopting equal weighting in the GIS-based spatial analysis has been clarified in Section 3.2. In this study, the GIS analysis was designed to evaluate the physical and spatial suitability of alternative locations based on objective data. Therefore, equal weighting was intentionally adopted as a neutral baseline to avoid introducing subjective bias during the initial assessment of spatial criteria.

In addition, the distinction between GIS-based analysis and the FUCOM–ELECTRE I model has been explicitly explained. While the GIS analysis focuses on physical and environmental suitability using a limited set of spatial criteria, the FUCOM–ELECTRE I model incorporates a broader set of operational, institutional, and infrastructural criteria, and determines their relative importance based on expert judgment. Thus, the two approaches differ in both weighting strategy and criteria scope, and are designed to serve complementary purposes rather than conflicting ones.

Furthermore, a comparison between GIS-based results and FUCOM–ELECTRE I outcomes has been included in the revised manuscript. The results suggest that equal weighting tends to emphasize overall physical and environmental suitability, whereas FUCOM-based weighting refines the prioritization of alternatives by reflecting operational and infrastructural considerations. This observation highlights that different weighting approaches may influence the prioritization of alternatives.

Comment (7) In spatial data processing, the datasets used vary in resolution and time span. The paper only mentions "standardization and harmonization processing" and notes that the spatial heterogeneity of some data is not obvious, but it does not specify the detailed processing methods or present the results of accuracy verification. The authors are advised to add details of data processing and an analysis of data accuracy.

Response: 

We have addressed the concerns regarding data quality, consistency, and reliability as follows:

The data preprocessing procedure has been described in detail in Section 3.2. Specifically, all spatial datasets were transformed into a common coordinate system (WGS 84 / UTM36N), clipped to the study area boundaries, and resampled to a consistent 30 m spatial resolution to ensure cross-layer compatibility. Appropriate resampling techniques- bilinear interpolation for continuous data and nearest neighbor for categorical data- were applied.

In addition, the reliability of the datasets has been clarified in the revised manuscript. Widely recognized and widely used data sources, including the General Directorate of Mineral Research and Exploration (MTA), WorldPop, and OpenStreetMap (OSM), were utilized. Relevant literature (Stevens et al., 2015; Fan et al., 2014; Küçük & Anbaroğlu, 2019) has been incorporated to support the suitability of these datasets for regional-scale decision-support applications.

Although a formal field-based accuracy assessment was not conducted within the scope of this study, the use of these widely adopted datasets, together with systematic preprocessing, contributes to the reliability of the analysis results.

Comment (8) In the ELECTRE I method, a 1–10 scoring scale was used for the decision matrix, but the basis and criteria for this scoring were not stated, leaving the subjectivity of expert scoring unconstrained. It is recommended to supplement the scoring rules and quantitative standards.

Response: 

In the ELECTRE I decision matrix, a 1–10 scoring scale was adopted, where 1 represents the lowest suitability and 10 represents the highest suitability. The scoring process was carried out based on predefined, criterion-specific evaluation rules. Each criterion (C1–C8) was explicitly defined, and the scoring principles were clearly defined and applied consistently.

Evaluations were conducted independently by experts based on their domain-specific knowledge and experience. For spatially measurable criteria, assessments were performed based on the relative performance of alternatives with respect to each criterion. For qualitative and semi-quantitative criteria, expert judgment was applied based on the literature, technical standards, and professional experience.

In addition, individual expert evaluations were subsequently aggregated using the arithmetic average to form the final decision matrix. This two-stage approach reduces subjectivity and ensures a systematic, transparent, and comparable scoring process.

Comment (9) The study results do not include an analysis of the contribution of each criterion to the location selection outcomes, making it impossible to identify the key influencing criteria. A quantitative analysis of criterion contribution is recommended.

Response: 

The influence of each criterion has been clarified in the revised manuscript. The role of the criteria has been interpreted by considering the FUCOM-derived weights together with their function within the ELECTRE I outranking process.

It has been explained that criteria with higher weights have a stronger influence on concordance indices and, therefore, play a more important role in determining the final ranking. The most influential criteria (particularly C1, C2, and infrastructure-related C3–C6) have been identified and presented in the Results section.

These revisions provide a clearer understanding of how each criterion affects the location selection outcomes and help explain the dominance of the top-ranked alternatives.

Comment (10) I infer that the study is quite extensive and the authors have split it into two papers; however, this leaves the reader with an incomplete understanding. For example, after the comparison of the two methods in Section 4.3, at least a consolidated result should be presented to the reader. Considering that a second paper may be forthcoming, the evaluation results in this paper can be brief, but they should not be omitted entirely.

Response: 

A consolidated interpretation of the results has been explicitly incorporated at the end of Section 4.3. In the revised manuscript, the comparative findings obtained from both GIS-based spatial analysis and the ELECTRE I method are synthesized and clearly presented in a unified manner.

Specifically, the revised section now provides a concise but explicit summary of the overall results, including the identification of the most suitable alternative (Boyabat) and a secondary viable option (Gerze), as well as the key differences between the two methodological approaches. This addition ensures that the reader can clearly understand the final outcome of the comparative analysis without requiring reference to a subsequent study.

While maintaining the scope of the current paper, the results are now presented in a more integrated and interpretable form, addressing the concern regarding incomplete understanding.

Comment (11) The study results do not show specific candidate locations within the alternative areas—only analyses at the district/county level were conducted, which limits the practical application value of the research. It is recommended to supplement the analysis with the screening of specific candidate sites within each district/county.

Response: 

The scope and level of analysis have been clarified in the revised manuscript. Specifically, it has been stated that this study focuses on macro-scale suitability analysis, where alternatives are defined at the district level based on the locations of existing university campuses, rather than as specific parcel- or site-level candidates.

Accordingly, the study aims to support strategic-level decision-making rather than detailed site-level implementation. To address the practical application aspect, it has also been clarified that the highest-ranking districts and the existing university-owned lands within these districts can serve as initial candidate locations for DRC implementation.

Furthermore, it is acknowledged that the identification of specific candidate sites within each district would require micro-scale analyses based on high-resolution data, and this has been indicated as a direction for future research.

Comment (12) The Discussion section is overly brief. As a methodological paper, there is much to elaborate on—including an evaluation of the proposed method, its advantages, a comparison with other methods, and its potential for wide application—all of which would require reference to more relevant studies. However, the current version lacks in-depth discussion on these aspects.

Response: 

The '4. Results and Discussion' section has been significantly expanded to provide a more comprehensive evaluation of the integrated Fuzzy FUCOM–ELECTRE I framework, as expected of a methodological study. The revised manuscript now includes a rigorous comparison with commonly used compensatory methods such as AHP and TOPSIS, as well as recent techniques like the Best-Worst Method (BWM). The methodological advantages of the non-compensatory ELECTRE I approach, particularly its role in ensuring the resilience of critical digital infrastructure, have been clearly elaborated. Furthermore, the robustness of the model and its suitability for macro-scale strategic decision-making have been clarified, supported by recent and relevant studies.

Comment (13) The study results are not linked to actual urban planning or disaster prevention planning, and the guiding significance of the results for the practical planning of Sinop Province is not explained. It is recommended to supplement the analysis and suggestions on aligning the study results with practical planning.

Response: 

The practical relevance of the study has been significantly strengthened by explicitly linking the results to real-world urban and disaster planning in Sinop Province.

A new subsection titled '4.6. Policy Implications' has been added to the Discussion section. In this section, the findings are translated into actionable recommendations for regional planning, infrastructure investment, and alignment with Türkiye’s Provincial Disaster Risk Reduction Plan. We have specifically detailed how the prioritization of districts like Boyabat and Gerze can guide local zoning, while the infrastructure gaps identified in Dikmen offer a roadmap for regional authorities and disaster management agencies.

In addition, a concise statement has been included in the Conclusion section to highlight the study’s practical contribution to sustainable urban development and resilient decision-making.

Comment (14) The proposed framework is claimed to be "scalable and replicable", yet the study only uses 5 districts/counties in Sinop Province as a case study and does not mention how the model can be adapted and adjusted for different regions or types of institutions. Supplementary explanations on the extended application of the model are recommended.

Response: 

A new subsection (Section 4.6. Policy Implications and Model Scalability) has been incorporated into the “Results and Discussion” section to clarify the scalability and replicability of the proposed framework. The revised manuscript now explains how the model can be adapted to different geographical regions by updating spatial datasets, and to various institutional contexts by redefining the evaluation criteria according to specific operational requirements.

Comment (15) The study limitations only mention issues related to data, the number of experts, and methodology, but neglect the limitations of the criterion system. It is recommended to add directions for improving the criterion system.

Response: 

The limitations of the criterion system have been explicitly addressed in Section 4.5 of the revised manuscript. A new paragraph has been added to clarify that, while the selected criteria focus on core physical and operational requirements, they may not fully capture broader dimensions such as economic costs, environmental sustainability (e.g., carbon footprint), and stakeholder-related factors. Future research directions for improving and expanding the criterion system have also been provided.

Comment (16) Future research recommendations do not mention the intelligent optimization of the model, such as integrating machine learning, big data and other technologies to refine the location selection model. Relevant suggestions for such future research are recommended.

Response: 

The Future Research section has been expanded to include the potential use of intelligent optimization techniques. Additionally, a new paragraph has been added to the Limitations section, explaining how machine learning and data-driven approaches could be utilized to improve the model's precision and adaptability in future studies.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All my problems have been solved. Congratulations to the authors.

Author Response

Comments 1: All my problems have been solved. Congratulations to the authors.

Response 1: We sincerely thank the reviewer for the positive feedback and kind words. We appreciate the time and effort devoted to evaluating our manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

Obviously, the authors have made detailed revisions to the paper, so I have no further concerns. It is recommended to continue improving the charts, especially the small annotations on the map, which may affect the reading effect.

Author Response

Comments 1: Obviously, the authors have made detailed revisions to the paper, so I have no further concerns. It is recommended to continue improving the charts, especially the small annotations on the map, which may affect the reading effect.

Response 1: Following the reviewer’s suggestion, the figures have been further improved to enhance readability, particularly by increasing the size and clarity of small annotations on the maps.
We appreciate the reviewer’s valuable feedback, which helped improve the overall quality of the manuscript.