Review Reports - The Design and Application of a Regional Management Model to Set Up Wind Farms and the Adaptation to Climate Change Effects—Case of La Coruña (Galicia, Northwest of Spain)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors. Thank you for your manuscript. It sheds light on the development potential of the La Coruña region in Spain in terms of wind energy. However, there are several comments and clarifications to your manuscript.

0. According to the iThenticate report, 16% and 5% of the Grammarly results of this paper contain auto plagiarism. The main works used are https://doi.org/10.1016/j.landusepol.2023.106644 and https://doi.org/10.3390/land12050944.

1. In the abstract, the authors write the following: ‘The aim of this study is to evaluate the current potential of the territory for the implementation of photovoltaic plants to make the wind energy use compatible with the environmental aspects, aggravated by the cumulative or synergic impacts, and social aspects’, which has no representation in the manuscript. Citation 52 should be removed from the manuscript because it is about solar energy. Use a paper related to wind energy to cite the methodology in section 2.3.3.

2. In the introduction, the authors point out the importance of air density in considering suitable locations for wind turbine construction because it affects energy production. However, this factor was not used despite the fact that the Global Wind Atlas provides such data.

3. The authors use ‘we’ and other personal pronouns in the article. It is necessary to change the text to avoid such formulations.

4. Why was one region of Galicia chosen rather than four? The data that were taken for the analysis are available for the whole country. Please explain your choice.

5. What is the novelty of this paper? The use of GIS and AHP is a recognised approach to solving the problem of finding new locations for wind farms. There are new approaches to solving this issue, such as using artificial intelligence or combining several multi-criteria analysis methods. None of them are applied in this paper and are not described in the introduction. Please explain your position.

6. Provide a summary table in section 2.2 indicating the data used in the paper and the format of the data.

7. Each abbreviation (e.g., HIC) is not clearly explained, making the text difficult to understand.

8. The paper states that public acceptance of new wind farms is very important, as well as how they affect the landscape visually. However, the paper does not use data that considers these factors. Please clarify.

9. Indicate in the results section the exact amount of area in square kilometres suitable for wind turbine construction.

The text has minor inaccuracies (e.g. QGiS version number) in section 2.3. Please check your manuscript for such inaccuracies.

Minor inaccuracies in the layout of the manuscript (see guidelines for authors).

Author Response

Estimado revisor, en primer lugar, nos gustaría expresarle nuestro más sincero agradecimiento por las valiosas observaciones y comentarios que ha compartido. Su experiencia y perspectiva son fundamentales para mejorar este trabajo. Estas recomendaciones sin duda enriquecerán significativamente los resultados y nos comprometemos a incorporarlas de la mejor manera posible. Agradecemos enormemente su tiempo y dedicación.

REVISOR 1

Según el informe de iThenticate, el 16% y el 5% de los resultados de Grammarly de este artículo contienen autoplagio. Los principales trabajos utilizados son https://doi.org/10.1016/j.landusepol.2023.106644 y https://doi.org/10.3390/land12050944.

En el artículo se citan cuestiones relacionadas realizadas por los propios autores, se ha realizado una nueva comprobación del texto cuidando que la información base no cambie significativamente:

Línea 89-95; Línea 168-175; Línea 207-209; Línea 223-231; Línea 256-264; Línea 273-284; Línea 304-321; Línea 336-337; Línea 375-399; Línea 411-412; Línea 573-582 y Línea 603-608.

En el resumen, los autores escriben lo siguiente: 'El objetivo de este estudio es evaluar el potencial actual del territorio para la implementación de plantas fotovoltaicas para compatibilizar el uso de la energía eólica con los aspectos ambientales, agravados por los impactos acumulativos o sinérgicos, y los aspectos sociales', que no tiene representación en el manuscrito. La cita 52 debe eliminarse del manuscrito porque se trata de energía solar. Utilice un artículo relacionado con la energía eólica para citar la metodología en la sección 2.3.3.

Línea 29. Se han eliminado el texto y la referencia del manuscrito y se han renumerado las referencias.

En la introducción, los autores señalan la importancia de la densidad del aire a la hora de considerar la ubicación adecuada para la construcción de aerogeneradores, ya que afecta a la producción de energía. Sin embargo, este factor no se tuvo en cuenta a pesar de que el Atlas Eólico Mundial proporciona dichos datos.

Líneas 107-126 . Hemos observado que efectivamente, quizá se requiera más información al respecto. Entre las líneas 105 y 123, se ha añadido el siguiente texto en relación con su comentario correcto ( En este documento se incorpora la nueva numeración de las referencias) :

<< Aunque tanto la variable densidad de potencia (expresada en energía eólica media sobre un metro cuadrado, W/m²) como la velocidad del viento (metros por segundo), podrían perfectamente ser variables explicativas de la idoneidad del territorio por disponibilidad de recurso eólico y disponibles de forma gratuita y pública en el Atlas Eólico Mundial, la densidad de potencia disponible podría proporcionar una determinación más real del potencial energético del territorio al utilizar velocidades instantáneas, en el corto plazo, por lo que se considera que esta variable debería ser utilizada a escala local. Por otro lado, a nivel regional, la velocidad media del viento se revela como la variable más consensuada por los expertos, el aerogenerador está diseñado para generar energía en un rango limitado de velocidades del viento, empezando por una velocidad de cizallamiento (aprox. 3-4 m/s), en la que no habría uso del recurso y terminando con una velocidad de cizallamiento (ca. 25 m/s) [17-18]. para salvar la integridad de la máquina y por seguridad en su entorno, circunstancias que se han tenido en cuenta en este trabajo. Los vientos están intensamente influenciados y reformados por la cobertura vegetal, las masas de agua, los patrones de uso del suelo, el terreno local, las condiciones climáticas y numerosas características adicionales [19], todas ellas consideradas en el estudio, por lo que la inclusión de esta variable, en interacción, contribuye a una estimación más detallada. Para la adaptación de la cartografía eólica utilizada a esta escala regional se han utilizado modelos estadísticos de reducción de escala [20], este método consiste en interpolar los puntos de cuadrícula más cercanos, en este caso, el Modelo Digital de Elevaciones (MDE) descargado del Catálogo Nacional de Información Geográfica recortado para la provincia de La Coruña a escala 1:25.000>>.

Líneas 169-176. 2.1. Área de estudio

Se ha añadido el siguiente texto en relación con su comentario correcto:

<<El área de estudio es la provincia de La Coruña, situada en el noroeste de la península Ibérica, dentro de la Comunidad Autónoma de Galicia (véase Figura 1). Está formada por 94 municipios, con una superficie total de 7.950,4 km², con elevaciones que van desde los 49 m de Mugardos hasta los 607 m de Toques. Según el Instituto Gallego de Estadística, la población en 2020 era de 1.121.815 habitantes, repartidos en 18 comarcas. Esto representa el 27% de la superficie total de Galicia y el 42% de su población. En 2022, se registraron más de 80 parques eólicos, según informa el Instituto Gallego de Estadística. La ubicación de los aerogeneradores, junto con las zonas de influencia legal y física, se ha considerado como un condicionante en este estudio>>.

The authors use ‘we’ and other personal pronouns in the article. It is necessary to change the text to avoid such formulations.

Quite rightly, the "we" has been removed in Lines 208, 268, 561-566,

Why was one region of Galicia chosen rather than four? The data that were taken for the analysis are available for the whole country. Please explain your choice.

Lines 26-33. We have observed that indeed, perhaps it requires more information in this regard. Between lines 105 and 123, the following text has been added in relation to your correct comment:

In the proposed framework, the province of La Coruña in the autonomous community of Galicia is tested, with a wide deployment of this type of infrastructure in the territory initiated in the 80s, representing the third autonomous community with the largest exploitation of wind resources, which provides sufficient information, extrapolated to the entire community, to demonstrate the practical usefulness and potential of the method of obtaining the territorial model proposed in this article The regional has been used as the basic administrative subunit of the study variables, considering that the territory thus delimited could have common physical and cultural characteristics.

What is the novelty of this paper? The use of GIS and AHP is a recognised approach to solving the problem of finding new locations for wind farms. There are new approaches to solving this issue, such as using artificial intelligence or combining several multi-criteria analysis methods. None of them are applied in this paper and are not described in the introduction. Please explain your position.

Lines 178-197 2.2. Data

<<The methodology adopted in this study involves the use of multivariate clustering and spatial analysis to identify suitable locations for the development of sustainable wind energy projects. This approach represents a significant advance in the field of site selection, combined with techniques agreed upon by the scientific community for the analysis of spatial data. The role of GIS and multi-criteria decision-making techniques (MCDC) as tools is well documented in the existing literature [23], [24], [27-32], [39-43]. Due to the fact that the result of territorial planning can vary substantially depending on the variables used for the hierarchical analytical process, the present work has a previous diagnosis of variables and criteria extracted from an extensive reference bibliography in which the variables most mentioned in published research that use multicriteria analysis and GIS for the location of optimal areas for wind ex-ploitation have been analyzed and that, in addition, could be more explanatory on a regional scale, specifically, for the province of La Coruña, in order to use criteria of suitability, impact, risk and climate change of homogeneous and in line with the scien-tific community, however, MCDC approaches face major challenges. However, MCDM approaches face several challenges. To address these issues, the integration of machine learning (ML) techniques holds promise. However, existing studies have not yet employed an independent ML-based approach for this purpose [44]. Artificial In-teligence (AI) could be a very powerful weapon in the sense of providing greater preci-sion in the data that feeds the variables, already proposed in an attempt to continu-ously improve over time [45]>>.

Provide a summary table in section 2.2 indicating the data used in the paper and the format of the data.

Line 198-199. The current official cartography in Spain offers a wealth of data.

A table has been added in Appendix 1 showing the data used.

Each abbreviation (e.g., HIC) is not clearly explained, making the text difficult to understand.

The assessment takes into account the catalogue of Habitats of Community Interest derived from community, state or regional legislation on nature conservation for the province of La Coruña, belonging to the National Habitat Inventory of Annex I of Directive 92/43/EEC. According to this Ministry, the Habitats Directive defines natural habitat types of Community interest as <"those natural and semi-natural areas, terrestrial or aquatic, which, in the European territory of the EU Member States, are threatened with extinction in their natural range, or have a reduced natural range due to their regression or because it is intrinsically restricted, or they constitute representative examples of one or more of the biogeographical regions of the European Union>".

The abbreviation HIC has been removed from Habitat Inventory Annex I of Directive 92/43/EEC (NHI):

Line 374. 3.2. Planning for territorial management categories.

The following coefficients and applied to the formulas was obtained: Impact model: (Criterion Proximity to National Habitat Inventory Annex I of Directive 92/43/EEC (NHI)

Line 741. Appendix A. Table A1. Data used, format and scale level

Priority Habitats of Community Interest (Annex I to Directive 92/43/EEC)

Line 751. Table A5. Most significant results of the studied variables.

National Habitat Inventory of Annex I of Directive 92/43/EEC (NHI) proximity

The paper states that public acceptance of new wind farms is very important, as well as how they affect the landscape visually. However, the paper does not use data that considers these factors. Please clarify.

In order to introduce the data provided in next explanation, a text has been added under this same heading:

Line 619-625. 4. Discussion

Within the environmental component that informs the territorial model, socioeco-nomic factors are included. The reduction in surface area associated with these factors results in an environmental cost for wildlife species, such as raptors and bats [65], as well as for landscape aspects due to the way the population inhabiting these areas perceives the landscape. This includes factors such as the agrological capacity of soils and forest influence zones. Specifically, 69% of the provincial forest area is comprised of plantation forests, according to the Forest Map of Spain.

Line 669-690. 5. Conclusion

<<The regional administrative unit was used as the basic subunit for study variables, as it is assumed that the areas defined this way share common physical and cultural characteristics. Since the results of territorial planning can vary considerably depend-ing on the variables used in the analytical process, this study is based on a preliminary diagnosis of variables and criteria drawn from an extensive reference bibliography. The study includes 26 variables, focusing on those most commonly agreed upon in ex-isting research, as well as proposals such as soil agrological capacity and visual quality of the landscape. These variables provide insight into the potential impact of wind en-ergy on local populations and feed into the territorial model created using mul-ti-criteria grouping techniques and GIS to identify optimal wind energy areas.

Soil classes of high quality for agriculture and forestry are often seen by the pop-ulation as an alteration to production and market structures, in addition to influencing the landscape’s visual quality. The coexistence of agricultural/forestry use and wind energy production is considered a priority in the study territory, though it is typically overlooked in similar research. The urban environment and cultural heritage are treated as legal restrictions in the regional protection framework. In fact, adaptation measures could prove more effective if they are integrated into existing local and sec-torial policies, which are applicable across Spain. The variables examined in this study enable the control and mitigation of a wind farm’s impact, aiding in territorial plan-ning decisions based on substantial evidence from official data, relevant to any part of the territory while adhering to current national and regional regulations. However, it is acknowledged that not all potential regulatory variables can be considered at this scale>>.

Indicate in the results section the exact amount of area in square kilometres suitable for wind turbine construction.

In order to introduce the data provided in next explanation, a text has been added under this same heading:

Line 462-477. 3.4. Territorial model by management categories

<<According to the zoning criteria defined in the previous section and the infor-mation obtained during the territorial diagnosis phase, 21% of the surface area has been excluded due to the presence of existing wind turbines. This exclusion, represent-ing the synergic or cumulative effect of other wind energy infrastructures, suggests that territorial capacity values could have been higher if not for this restriction. This variable will be an indicator of the degree of implementation of these infrastructures over time, which complements the model. On the other hand, this exclusion percentage rises by 5% when considering regulatory or legal areas, such as protected zones within the Natura 2000 Network, and by 4% for areas of cultural interest. Additionally, the distance to population centers, defined as areas with stable human occupation, is considered a restrictive factor in Galicia. A minimum distance of 500 meters is imposed from urban or potentially developable land, which significantly increases the total ex-cluded area. However, the main issue highlighted in this study is that the available population entity maps for the province of La Coruña include 17% of the 7,486 entities with no registered inhabitants as of 2023, while 62% show records of fewer than 50 inhabitants per entity, which reflects significant depopulation in rural areas. While this indicator is not the focus of the paper, it could be an important factor for future research. Consequently, the distance to population centers requires further investiga-tion. In the most restrictive exclusion scenario, considering the full value of this varia-ble, the total excluded area reaches 273.41 km², representing 66% of the total excluded area within the province>>.

Line 512-523.

<<Figure 6 shows in the least restrictive exclusion scenario (30% of the total target area), an acceptable area for the construction of wind farms of 3,734.4 km2, not in-cluding the distance to population centers, restristed in an area of influence of 500 m, considering that this variable is unapproachable at the regional level and must be con-sidered by the promoter of this type of infrastructure at the local level, attaching to the construction project, socioeconomic and environmental studies with specific meas-urements of noise and shadow effect for specific commercial wind turbines [63]. In the most restrictive exclusion scenario (66% of the total target area), which would include the distance to population centres in an area of influence of 500 m, the acceptable area for the construction of wind farms would amount to 1,841.5 km2, 23% of the total area of the province of La Coruña, while the unacceptable areas proposed for conservation and restoration would amount to 273,4 km2 and 551,84 km2, respectively>>.

The text has minor inaccuracies (e.g. QGiS version number) in section 2.3. Please check your manuscript for such inaccuracies.

Line 231-232. , 2.2. Data

…asumiendo que las emisiones se mantienen altas. Se utilizó el software QGIS 3.40.1 GRASS 7 con datos (ETRS89 / UTM 29N).

Pequeñas imprecisiones en el diseño del manuscrito (ver pautas para autores).

Se ha revisado la bibliografía, se han incorporado y se incorporan al texto estudios recientes publicados en nuevas investigaciones en 2023 y 2024 sobre planificación territorial de parques eólicos e inteligencia artificial. A continuación se muestran las nuevas referencias y la renumeración para su incorporación con comentarios al texto del manuscrito.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper focuses on the design and implementation of a regional management model for establishing wind farms in La Coruña, Galicia, addressing both climate change adaptation strategies and environmental considerations. The manuscript is well-written and systematically organized, with a clear presentation of the methodology and results. However, the following suggestions can help enhance the overall quality of the paper:

The introduction section should be improved by incorporating recent and relevant references to provide a stronger context and highlight the novelty of the study.
The specific contributions of the paper should be clearly articulated, preferably in bullet points, to emphasize the unique aspects of the research.
All equations used in the paper should be appropriately cited with relevant references to ensure proper attribution and alignment with existing literature.
The current subtitle "2.3.1" may not fully align with the content it describes. If possible, consider revising it to make it more descriptive and suitable.
The conclusion section is currently too general. It should be refined to summarize the key findings and their implications. Additionally, the main results should be explicitly highlighted to reinforce the impact of the study.

Author Response

Dear reviewer, first of all, we would like to express our sincere thanks for the valuable observations and comments you have shared. Their experience and perspective are critical to improving this work. These recommendations will undoubtedly significantly enrich the results and we are committed to incorporating them in the best possible way. We greatly appreciate your time and dedication. Answers are highlighted in blue.

REVIEWER 2

The introduction section should be improved by incorporating recent and relevant references to provide a stronger context and highlight the novelty of the study.

The bibliography has been reviewed, recent studies published in new research in 2023 and 2024 on territorial planning of wind farms and artificial intelligence have been incorporated and incorporated into text. Below are the new references and the renumbering for their incorporation with comments on the text of the manuscript.

Oras Fadhil Khalaf, Osman Nuri Uçan, Naseem Adnan Alsamarai et al. Utilizing Multivariate Clustering and Spatial Analysis for Selecting Suitable sites for Sustainable Wind Energy, 03 April 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/1 .21203/rs.3.rs-4158696/v1]

Siamak Moradi, Hossein Yousefi, Younes Noorollahi, Diego Rosso,Multi-criteria decision support system for wind farm site selection and sensitivity analysis: Case study of Alborz Province, Iran, Energy Strategy Reviews,Volume 29,2020,100478,ISSN 2211-467X,https://doi.org/10.1016/j.esr.2020.100478.

Khan, K.I., Nasir, A. Application of artificial intelligence in solar and wind energy resources: a strategy to deal with environmental pollution. Environ Sci Pollut Res 30, 64845–64859 (2023). https://doi.org/10.1007/s11356-023-27038-6

Atakan Bilgili, Tümay Arda, Batuhan Kilic, Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms, Energy Conversion and Management, Volume 309, 2024, 118441, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2024.118441.

Atienza, J.C., I. Martín Fierro, O. Infante, J.Valls and J. Domínguez. Guidelines for the assessment of the impact of wind farms on birds and bats (version 3.0). SEO/BirdLife, Madrid, 2011. Available at: PORTADA MOLINOS-3.0

The specific contributions of the paper should be clearly articulated, preferably in bullet points, to emphasize the unique aspects of the research.

Lines 700-731

The main contributions of this study are as follows:

The development of a territorial model that incorporates risk mitigation over a broader time frame, adapting to climate change. This approach considers various scenarios in territorial planning, whether caused by internal natural processes, changes in external factors, or persistent anthropogenic changes to the atmosphere or land use. The inclusion of this climate change factor aims to reduce the vulnerability of the territory, should the acceleration of extreme atmospheric conditions predicted in publicly available climate change models become a reality.
For evaluating climate evolution trends, the study also considers population trends, based on official data from 2019-2035. These results are comparable with short-term or stabilization climate change scenarios, where temperatures are expected to remain below a 2°C increase.
The capacity values obtained, which are considered low to moderate on the proposed scale, are partly due to the exclusion of 21% of the region's total area in the territorial model. As a result, it is logical that the remaining areas may have a lower carrying capacity, extending to 66% in the most restrictive scenario, especially when considering population entities.
It was found that different climatic and population trends affect the data on ecosystem and territorial reception capacity. Changes in population levels significantly influence regional energy consumption, meaning that adaptation measures will prioritize areas based on projected shifts in regional energy costs.
Socioeconomic criteria are considered crucial in implementing this type of infrastructure, particularly due to the population's visual perception of the landscape and cultural factors that are not often taken into account in such evaluations.

This study can serve as a valuable reference for the development of strategic plans for renewable energy acceleration zones. It helps moderate potential damage from their implementation or take advantage of opportunities linked to climate change, where prior knowledge guarantees predictability. Additionally, this knowledge can simplify administrative procedures for granting streamlined authorizations, ensuring equitable access to energy for the common good. This study is also useful for revising early warning plans in disaster risk management, ecosystem management, and the creation of local climate change adaptation plans.

All equations used in the paper should be appropriately cited with relevant references to ensure proper attribution and alignment with existing literature.

All the equations have been properly referenced. Thank you.

The current subtitle "2.3.1" may not fully align with the content it describes. If possible, consider revising it to make it more descriptive and suitable.

On line 257, the title of the section has been corrected. 2.3.1. Variables selected from previous literature

The conclusion section is currently too general. It should be refined to summarize the key findings and their implications. Additionally, the main results should be explicitly highlighted to reinforce the impact of the study.

The conclusions section has been expanded, as can be seen in lines 641-730

Reviewer 3 Report

Comments and Suggestions for Authors

The Design and Application of a Regional Management Model to Set up Wind Farms and the Adaptation to Climate Change effects. Case of La Coruna (Galicia, Northwest of Spain)

TIn this study, The authors propose a territorial management model to establish wind farms in La Coruña, Galicia, addressing the challenges of climate change. They emphasize the need for specialized studies in accordance with Law 21/2013 and Royal Decree-Law 6/2022, as well as strategies aligned with EU regulations and local attitudes toward energy efficiency. The importance of adhering to regulatory frameworks, conducting environmental impact assessments, and engaging the community is highlighted. Their goal is to minimize environmental impacts and streamline project planning and implementation.

Major:

1. Abstract: Error on line 29, the authors talk about photovoltaic plants in the objective of the study.

2. Introduction: Add specifically the contributions and the structure of the article.

3. Materials and Methods: Justify the use of AHP and not another. It is a very subjective method when experts intervene.

4. Results and discussions: Although the case study is well presented and explained, the section falls short in depth due to the lack of discussion and analysis compared to other similar studies.

5. Conclusions: Include in the conclusions if it is possible to extrapolate the study and future lines of research.

Author Response

REVIEWER 3

Abstract: Error on line 29, the authors talk about photovoltaic plants in the objective of the study.

Line 29. Text and reference have been removed from the manuscript and the references have been renumbered.

Introduction: Add specifically the contributions and the structure of the article.

All sections of the text have been revised, and we hope that everything is now clearer.

Materials and Methods: Justify the use of AHP and not another. It is a very subjective method when experts intervene.

Lines 174-193 2.2. Data

The methodology adopted in this study involves the use of multivariate clustering and spatial analysis to identify suitable locations for the development of sustainable wind energy projects. This approach represents a significant advance in the field of site selection, combined with techniques agreed upon by the scientific community for the analysis of spatial data. The role of GIS and multi-criteria decision-making techniques (MCDC) as tools is well documented in the existing literature [23], [24], [27-32], [39-43]. Due to the fact that the result of territorial planning can vary substantially depending on the variables used for the hierarchical analytical process, the present work has a previous diagnosis of variables and criteria extracted from an extensive reference bibliography in which the variables most mentioned in published research that use multicriteria analysis and GIS for the location of optimal areas for wind exploitation have been analyzed and that, in addition, could be more explanatory on a regional scale, specifically, for the province of La Coruña, in order to use criteria of suitability, impact, risk and climate change of homogeneous and in line with the scientific community, however, MCDC approaches face major challenges. However, MCDM approaches face several challenges. To address these issues, the integration of machine learning (ML) techniques holds promise. However, existing studies have not yet employed an independent ML-based approach for this purpose [44]. Artificial Inteligence (AI) could be a very powerful weapon in the sense of providing greater precision in the data that feeds the variables, already proposed in an attempt to continuously improve over time [45].

Results and discussions: Although the case study is well presented and explained, the section falls short in depth due to the lack of discussion and analysis compared to other similar studies.

There are numerous studies in the existing literature on the relationship between territorial planning, optimal zoning for the location of wind farms, but no references have been found on those that integrate 19 variables into the model, discarding 6 variables that, although they have been studied, have not been considered explanatory, and the main novelty of the study, The mitigation of natural, anthropogenic and climate change risk is incorporated into the reception capacity model with a selection of explanatory variables evaluated for this study. There are no known references, even in the 2024 revision to establish comparative results. In summary, there are no similar studies, with the number of variables and criteria used in the study, although they are, using the same GIS and multi-criteria data processing techniques that have been incorporated into the manuscript and are highlighted in blue. However, reference is made to AI AI has been incorporated as a future field of research that could provide greater accuracy and reliability in real-time data.

The conclusions section has been expanded, as can be seen in lines 641-730

Conclusions: Include in the conclusions if it is possible to extrapolate the study and future lines of research.

This methodology is a proposal aimed at future application through artificial intelligence, considering the factors utilized. However, for now, we wanted to determine whether it works or not.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors:

Thank you very much for the changes made, the article has improved significantly in terms of scientific quality.