(R-ISSUES) Rural Interoperable System of Systems for Unified Environmental Stewardship

Abstract

Spain has one of Europe’s most extraordinary biodiverse environments and a significant risk of fires in its forests. At the same time, rural areas are affected by several challenges, such as desertification, population decrease, and loss of income. Fortunately, some green sparks arise on the horizon. Among them, we use critical enabling technologies for fire prevention and extinction, renewable energy, and resilience solutions by adopting a system of systems approach given by the systems engineering frameworks. We analyse recent Research and Development (R&D) projects focused on fire prevention to detect (1) the key enabling technologies used and (2) engineering practices. A motivational case is presented, which evaluates the potential interest of the mineral water industry sector in applied R&D with key enabling technologies, including the replicability potential and the replicability potential for using the R&D results. After that, the authors initialize an innovative R-ISSUES model to promote early interoperability between energy and environment systems engineering towards the shared mission of designing digital and rural jobs to preserve our biosphere. The model is used to answer specific research questions and detect gaps or potential improvements for the model itself by using the recent scientific literature.

1. Introduction

Although vulnerable, Spain has one of the most critical biodiverse environments because of the different climate conditions. In the Iberian Peninsula and other Mediterranean regions, the effects of forest fires are constant, and they are a significant risk to biodiversity. This partly motivates new regulations in the European Commission (EC) for restoration. Among other objectives, it aims to rehabilitate at least 20% of the terrestrial and marine areas of the European Union (EU) by 2030 and all degraded ecosystems by 2050 [1]. Conversely, solutions like reforestation are also helpful for adaptation and mitigation to climate change, alimentary security, and rural development.

The Spanish population is concentrated in cities and city suburbs. The rural areas are suffering from workforce scarcity, and, at the same time, their inhabitants are aging. To attract youth talent to rural areas, the labour offer should adapt to young people’s interests and skills as much as possible, and this can be the case for digital businesses for nature and biodiversity conservation, at least as long as digital technologies are demonstrated to be helpful, reliable, affordable, and challenging. Key enabling technologies (KETs) were introduced in 2018 and by the European Parliament in 2021 [2]. The EU’s strategic approach to industrial research and innovation defines key enabling technologies (KETs) complementing the European Research Area policy agenda (ERA) [3]. Regarding KETs, the European Commission has prioritized life science technologies, artificial intelligence, security and connectivity, and other categories. Regarding the ERA, Horizon Europe partnerships bring the EC and private and public partners together to address some of Europe’s most pressing challenges. This includes the green transition and the digital and industrial transition in general [4], renewable energy solutions, sustainable water and food systems, and leadership in key digital technologies. In the equator of the Horizon Europe Program (2021–2027), there should be several Research and Development (R&D) projects covering such challenges.

The main question follows. With the main objective of defining a pilot project to carry out, focusing on biodiversity and the use a specific set of key enabling technologies, what if we combine such challenges with industrial leadership regarding forest fire risk? First, we must focus on one industrial sector vulnerable to forest fire risk. This sector could be the mineral water industry. We also need a common challenge for energy and water/food systems and the selected industry, like resilience, and joint digitalization opportunities like Building Information Modelling (BIM). However, combining challenges involves combining disciplines and increasing complexity, so we must adopt a system of systems (SoS). Open literature works covering this topic are lacking to the authors’ knowledge. The aim of this study is to shed some light on this issue, covering it in Section 1.1, Section 1.2 and Section 1.3. Furthermore, Section 1.4 introduces forest fire risk for the selected industry, resilience engineering, BIM’s role, and the SoS approach. A graphical summary of this introduction is given in Section 1.5.

1.1. Forest Fire Risk for Mineral Water Sector

Multiple causes of wildfires exist, from human to natural ones [5]. Several starting and propagation mechanisms exist, such as easily burning trees, dry residues, hot metal particles [6,7], and many others studied by researchers for decades. Digital enablers like artificial intelligence (AI) are also being used to expand the governing physics to the real world, where complex geometries and multiple causes may coexist [8].

Mineral and spring water activities are typically conducted in wild forests. They have been officially regulated in Spain by the Real Order 1798/2010 and updated to fit the European regulations [9]. They establish the collection, handling, circulation, and marketing rules. This regulation considers the risk of contamination and the existence of a protection perimeter, where certain activities that may negatively influence the resource are prohibited or conditioned to safeguard it. Some companies in the sector collaborate with the regional forest management plans with the aim that the forests under which the aquifers are located continue to be the best natural protectors, preserving the native vegetation, in addition to collaborating with the cleaning of the diverse and multiple forests so that they are biodiverse [10].

According to the last decennial (2006–2015) report on forest fires in Spain [11], around 13,111 fire events affect 32,028 hectares (ha) of wood yearly on average and 100,796 ha of forest. The number of events with less than 1 ha was 8651 (4460 of >1 ha). In 1996, the Spanish inventory of the total forestalled surface of Spain was 50,596,014 ha, 10,625,698 ha of wood [12]. This measures the yearly probability of having a forest fire in woody and forest areas, which is 0.06% and 0.95%, respectively, and, of course, there are regions with more risk than others. There can be several consequences of having a fire in forest areas next to the watershed protection area. A known result is a potential reduction in rainwater infiltration because of the ground compaction. The absence of vegetation may increase the risk of flooding but also have other effects [13]. These consequences could last for years. The Alliance for Water Stewardship (AWS) developed the AWS Standard [14], a globally applicable framework engaging water-using sites to address a site’s water risks and opportunities. The AWS Standard certification requests a continuous dialogue with the site’s stakeholders to evaluate risks and demonstrate responsibility and resilience. Some digital enablers may help to achieve resilience to forest fire, from new Unmanned Aerial Vehicles (UAVs) equipped with specific sensors with a square meter resolution for risk assessment and rescue operations [15] to satellites providing fire alerts in minutes at the hectare resolution [16].

1.2. Resilience Engineering

Resilience is the ability to provide the required capability when facing adversity. Avoiding adversity is considered a way of achieving resilience but not the only one. To engineer other resilience solutions, it is possible to use some existing taxonomies covering fundamental objectives and different objectives, such as architecture, operational processes, and a set of type requirements [17]. In practice, the industrial resilience of a manufacturing system can be achieved using tools and techniques within the context of an operational resilience framework to ensure business continuity. Risk management helps organizations identify, assess, and mitigate the potential risks that could disrupt their business. Risks and opportunities can be managed together, too.

In the case of a mineral water plant, as in many other industrial plants, electricity curtailment is a risk that can be reduced, at least partially, by generating and storing electricity. The absence of an accessible water reservoir for fire fighting near the forest is a risk. This risk can be reduced by filling an artificial reservoir regularly. This requires access to an electric supply. Let us suppose this risk can be reduced by using other industrial risk reduction measures. In that case, this is an opportunity for industrial leadership in forest stewardship, which is to collaborate with public owners of the forest in early fire detection. Both are examples of resilience solutions that require specific engineering. A particular case of building resilience is forecasting fire propagation inside a crisis committee and defining strategies in almost real time. Conducting such simulations previously may define strategies for minimizing other long-term impacts on underground water resources. This is according to recent projects based on GIS to assist the civil protection authorities of Portugal and Spain at the national, regional, and local levels [18] or more recent ones using Big Data and advanced modelling tools shared with other environmental domains [19].

1.3. Building Information Modelling (BIM) and Geographic Information System (GIS)

Engineering solutions for resilience, like an on-ground fire detection station, a vigilance centre, a water pumping station, or a water pipeline, need digital solutions for defining materials, geometries, constructive elements, and collaboration. This is the aim of Building Information Modelling (BIM). It covers the whole lifecycle of such assets, from the idea to the end of life [20]. BIM is also being requested in the public contracting of civil works. BIM industrialization drives early to prefabricated solutions or shorter delivery times, too, which is interesting for attracting young people to rural areas.

BIM technology is used throughout the lifecycle of an asset, and different dimensions can be developed depending on the associated information, in addition to 3D, 4D time [21], 5D cost, 6D sustainability [22], or 8D health and safety [23]. This requires collaboration between different professional profiles and the management of increasingly diverse information. On the other hand, it is necessary to standardize collaboration processes according to regulations such as ISO 19650 [24] and the standardization of information exchange under the standard and open Industry Foundation Classes (IFC) data model. An example is the energy behaviour of buildings [25], although this is not covered by data interoperability throughout the entire asset lifecycle and facility management or the facility’s industrial processes.

Integrating richer information is possible by combining IFC with ontology, such as in [26,27], enabling the connection between the IFC data and other data. Jia et al. [27] pointed out the challenges and opportunities of IFC and ontology integration (IFCOI) by proposing a building lifecycle management model based on IFCOI. Thus, the IfcOWL data model appears to be a scheme between IFC and Web Ontology Language (OWL). Researchers such as Wang et al. [28] proposed the conversion and validation of the WHERE rules used to define data integrity constraints in the IFC scheme.

The integration of information from BIM models and Geographic Information Systems (GISs) is increasingly common, but this requires the integration of IFC mapping and the city geometry markup language (CityGML) standard, placing particular importance on ontology and semantic mapping [29].

1.4. System of Systems (SoS) Engineering

Systems engineering (SE) is a methodological, multi-disciplinary, integrative approach to engineering systems. The discipline is described in the International Council on Systems Engineering (INCOSE) handbook adapted to the ISO 15288 standard [30].

A system of systems (SoS) comprises systems with at least two conditions: operational independence, managerial independence, geographical distribution, emergent behaviour, or evolutionary development processes [31]. Each of the constituent systems of the SoS may have its lifecycle, which complicates the interoperability of the SoS’s engineering. Specific Information and Communication Technologies (ICT) offer interoperability and configuration management capabilities [32], which help solve this SoS engineering problem, generally in an iterative manner [33].

ISO 21839 [34] provides a definition of SoS and its constituent system as follows:

System of Systems (SoS)—A set of systems or system elements that interact to provide a unique capability that none of the constituent systems can accomplish independently. Note: Systems elements can be necessary to facilitate the interaction of the constituent systems in the system of systems.

Constituent Systems—Constituent systems can be part of one or more SoS. Note: Each constituent is a valuable system, with its development, management goals, and resources, but it interacts within the SoS to provide the SoS’s unique capability.

The definitions of SoS are diverse and depend on the particularity of an application area. The types of SoS can be directed, acknowledged, collaborative, or virtual. Each is based on the degree of independence of constituents, origin, and relationships. This is the Taxonomy of Systems of Systems presented in 15288 Annex G and ISO 21841 [35].

Systems of systems are generally characterized as complex [36,37,38,39,40,41], as noted in the system of systems (SoS) knowledge area of the SEBoK [42]. According to INCOSE, dealing with complexity needs system thinking principles, such as meta-cognition, encouraging variety, considering the relationships of the possible solution, and humility. A combination of courage and humility enables the complex systems engineer to risk genuine innovation and learn fast from iterative prototyping of solutions in context [42].

1.5. Summary and Research Questions

Figure 1 provides a graphical representation of a conceptual model for combining challenges in energy and environment for industrial leadership based on the related concepts described in Section 1.1, Section 1.2, Section 1.3 and Section 1.4, complemented with some assumptions (A) related to single or multiple relationships of the model.

As can be observed in Figure 1, resilience is a common challenge that benefits from key digital technologies (like BIM) and SE practices. To do that, there is a need for digital and knowledge workers where the industrial leaders are, at least for demonstration but also for using and maintaining the systems that provide such resilience. An Energy, Environment, and Engineering Rural Action (3ERA) is required to build that capacity, and the rational starting point is to learn from demonstration or a build-up project.

As mentioned in Section 1, it shall be demonstrated that the selected digital technologies are useful in practice, reliable, affordable, and challenging. In the first demonstration approach, some generic research questions have been formulated to validate and improve the model.

Table 1. Question correspondence to the model’s relationships, Assumptions and objectives.

High-Level Objectives	Question	Conceptual Model’s Relationships	Assumption	Strategy
Digital Technologies can be useful and challenging.	(Q1) Is BIM interoperating with other KETs?	Open data for Interoperability (IFC) needs to interoperate with other disciplines.	A3—BIM uses the IFC standard, but several efforts involving different ontologies are still necessary to enable interoperability. Systems engineering describes the Interoperability Management process [30], which covers semantic and technical interoperability.	Recent R&D and innovation projects and expert analysis using ERA categories.
Digital Technologies can be reliable.	(Q2) Is BIM being used for building resilience?	Resilience Engineering frameworks can be used to (examples of) systems providing resilience to fire risk. Examples of systems providing resilience to fire risk using Building Information Modeling.	A2—For engineering resilience, we need digital and collaborative solutions, which is the aim of Building Information Modeling (BIM).	Bibliographic research and Natural Language Processing, and expert analysis.
Digital Technologies can be affordable.	(Q3) How can replicability of a set of solutions for resilience be assessed in practice?	Demonstrate responsibility and resilience to water risk benefits from Resilience Engineering frameworks.	A1—Water Stewardship can benefit from Resilience Engineering frameworks because the diversity of solutions for resilience needs to be engineered.	Economic assessment and sample statistics.
Digital Technologies can be reliable.	(Q4) Is investing in resilience in the investment scope of private firms?	Mineral and spring water industrial business (in an) object of Water Stewardship certification. Systems providing resilience to fire risk include early fire detection, water storage for extinction, and electricity storage.	A4—Engineering systems providing resilience can benefit from a System of Systems (SoS) approach.	Bibliographic research and Natural Language Processing, and expert analysis.

shows the correspondence of the questions (Q) with the conceptual model relationships, the main assumptions, the strategy to answer the questions, and the high-level objectives.

Regarding assumption A4, apart from the intention of combining more than one system, it is necessary to justify more about the compliance to the definition of SoS given in Section 1.4.

Table 2. Systems providing resilience and SoS characteristics mapping.

Solution for Resilience for Forest Fire Risk Event	Operational Independence	Managerial Independence	Geographical Distribution	Emergent Behaviour or Evolutionary Development Processes
Early fire detection system	The systems are operated independently using their respective processes but can be operated with the same staff if adequately trained. The electricity supply and other risks, like cyber threats, make these systems interdependent, too.	Requires collaboration with different Public Administrations.	Satellites and masts are used to cover the whole watershed.	Ground detection may grow in time, depending on the actual satellite resolution.
Water storage system for fire fighting			Water reservoirs.	The water reservoir could be used to produce electricity in some sites.
Electricity storage system		Requires collaboration with electric network operators.	Next to the electric feed-in, next to the factory.	Depending on renewable generation, it can grow to provide resilience to the main factory’s activities.

shows this.

The previous questions are expected to be solved with the Materials and Methods described in Section 2.

2. Materials and Methods

Demonstrating digital technologies can be useful but challenging, and to try to answer Q1 according to the strategy, it is necessary to investigate the EU CORDIS Research and Development (R&D) database to detect the use of some key enabling technologies (KETs) in energy, water, or food R&D projects. We need to see if such projects are using systems engineering (SE) practices like those described in the International Council on systems engineering (INCOSE) handbook [30]. Section 3 shows a classification based on ERA challenges and the authors’ criteria to provide results

Once KETs and SE have been assessed, it makes sense to explore a motivational example of the economics of a mineral water plant to be involved in R&D projects and the replicability potential for using R&D results in Spain. As a motivational case study, a simplified, integrated cost model is used to assess the feasibility of conducting such R&D by a theoretically leading mineral water plant in Spain. The model is calibrated with the AWS reports [9] and complemented with a Geographic Information System (GIS) analysis. This is shown in Section 4. This is necessary to demonstrate that digital technologies can be affordable and to try to answer Q3 according to the strategy.

With the previous research and motivational case, it is possible to introduce a model called R-ISSUES to promote early interoperability between energy and environment systems engineering towards the shared mission of designing digital and rural jobs and preserving our biosphere, corresponding to the motivational case. This is necessary to demonstrate that digital technologies can be reliable (in the sense of allowing engineering knowledge reusability) and to try to answer Q2 and Q4 according to the strategy. The model is described in terms of an ontology. The ontology can be expanded later to answer the research questions (Q) to detect gaps, trends, or improvements using the ScienceDirect™, Amsterdam, The Netherlands, database and recent Natural Language Processing methods described in [43] in Section 5 and Section 6.

3. KETs and Research and Development (R&D) Projects

3.1. R&D Project Search

The authors conducted a first search of European-Commission-funded Research and Development (R&D) projects to detect the current trends considering the use of Building Information Modelling (BIM) and Geographical Information System (GIS). The public CORDIS database was used to search for projects from Horizon 2020 and Horizon Europe programs using the following “Domain of Application”, covering the ERA topics presented in the Introduction:

• Food and Natural Resources
• Digital Society
• Energy

Table 3. R&D projects (with literal contents).

ID	Name	Summary	End Date
779962	V4Design	V4Design developed a platform that provided architects, video game creators, and designers with the innovative tools necessary to simplify the design process during the creative phase.	31 March 2021
636717	IMPRESS	IMPRESS developed different prefabricated panels for buildings. An innovative manufacturing process was created to create the panels, including reconfigurable moulding (RM) techniques, 3D laser scanning, and 3D printed technology. The overall manufacturing process allows mass production of panels, considers complex architecture and allows for faster production while lowering prefabrication costs.	31 May 2019
665220	INCEPTION	INCEPTION realized innovation in 3D modelling of cultural heritage using an approach for time-dynamic 3D reconstruction, built, and social environments.	31 May 2019
636063	INSITER	INSITER aims to eliminate the gaps in quality and energy performance between the design and realization of energy-efficient buildings based on prefabricated components. The key innovation of INSITER is the intuitive and cost-effective Augmented Reality that connects the virtual model and the physical building in real time. The project developed a new methodology for self-instruction and self-inspection.	30 November 2018
792073	HyCool	HyCool Project proposed the coupling of a new Fresnel CSP Solar thermal collectors with specially built Hybrid Heat Pumps (a combination of adsorption and compressor-based heat pumps) for a wider output temperature range to provide a wide range of design and operational configurations increasing the potential implementation of the proposed Solar Heat in industrial environments.	31 October 2022
680676	OptEEmAL	OptEEmAL aimed to develop an Optimised Energy Efficient Design Platform for refurbishment at the district level to deliver an optimized, integrated and systemic design based on an Integrated Project Delivery (IPD) approach for building and district retrofitting projects.	28 February 2019
681673	L.I.F.E.	LIFE offered a complete set of archaeological and environmental data to investigate Late Roman settlements. A combination of classic and innovative investigation techniques was carried out within the wider frame of an environmental study of the area and a historical analysis of the textual sources.	28 February 2023
775971	Smart Exploration	Smart Exploration consisted of a research and application team supported by a group of technologically advanced SMEs and the mining industry. The project developments will generate new technological and methodological markets in Europe and result in improved exploration strategies in Europe and beyond.	30 November 2020

shows the results.

3.2. R&D Project Analysis

As mentioned in Section 1.5, the INCOSE guidelines describe a set of processes for designing, implementing, and maintaining better systems. Among them are the interoperability management processes that are key for the different stakeholders, systems, subsystems, and parts to understand the information exchange, which is necessary at any stage of the system’s lifecycle and with the system of systems to which the system may belong.

Table 4. Evidence of interoperability and other systems engineering practices (with literal content).

ID	BIM Interoperability and Other KETs	Other Systems Engineering Processes (Requirements Management at Different Levels, Validation and Verification, …)	Interoperability Management
779962 [44]	The project delivered the basic version of the ontologies to represent building-related resources (BIM/GIS), as well as the linked data design for the actual population and querying of the database.	The project used technical requirements, architecture, and functionalities of the V4Design platform.	During the “Definition of ontologies and linked data setup,” the ontologies representing relevant resources were defined, as well as the linked data for the database’s actual population and querying. In addition, the advanced version of the semantic models adapted or extended according to the project’s requirements was presented.
636717 [45]	The project integrated a BIM cloud-based database, focusing on the interoperability between software tools required for the prefabricated process.	Through an in-depth stakeholder analysis, the project baselined existing collaborative methodologies and investigated new penalty-based business models. The final result was demonstrated on two existing buildings, where the final as-built product performance will be validated against the initial design.	A new BIM-based Iterative Design Methodology was produced, incorporating all stages of the design—install—operate process. It was integrated into an online management platform (a cloud-based BIM) database, which focused on the interoperability between software tools required for the prefabricated process.
665220 [46]	INCEPTION solved the shortcomings of state-of-the-art 3D reconstruction. It solved the accuracy and efficiency of 3D capturing by integrating Geospatial Information, Global and Indoor Positioning Systems (GIS, GPS, IPS) through hardware interfaces and software algorithms.	The Consortium was fully supported by a Stakeholder Panel representing an international organization (UNESCO), European and national public institutions, and NGOs in all fields of cultural heritage.	INCEPTION methods and tools resulted in 3D models easily accessible for all user groups, and interoperable for use by different hardware and software. It developed an open-standard Semantic Web platform for Building Information Models for Cultural Heritage (HBIM) to be implemented in user-friendly Augmented Reality (VR and AR) operable on mobile devices.
636063 [47]	INSITER enhanced the functionalities and capabilities of measurement and diagnostic instruments using a smart Application Programming Interface (API) and data integration with a cloud-based Building Information Model (BIM).	INSISTER used an inclusive approach: time dynamics of 3D reconstruction; addressed scientists, engineers, authorities and citizens; and provided methods and tools applicable across Europe.	INSITER leveraged the energy-efficiency potentials of buildings based on prefabricated components, from design to construction, refurbishment, and maintenance. It scaled up the use of BIM for standardized inspection and commissioning protocols, involving all actors in the value chain.
792073 [48]	Project Task 9.7 “Using BIM, SCADA, 3D and AR/VR Digital Twins based on GIS-BIM for Communication & Dissemination activities”. The deliverable D9.8 Report on the “Virtual Reality based on BIM models” of the H2020 HyCool project produced within the context of WP9 “Dissemination and Communication” indicates the links the remote access to the VR/AR and other 3D-BIM-GIS-SCADA based virtual environments and supporting materials to be used by the project partners and pilot owners to exploit, disseminate and communicate the project results, helping to promote further commercial actions.	The project conducted a validation of the developed model for lab-scale experiments. The validation aimed to demonstrate that the model developed could predict real-scale behaviour.	The public deliverable D8.6 Report on VR/AR Intelligent Training Systems based on Geolocated BIM models explains the capabilities and potentialities of using the digital design of the project pilots and the possibility of using them for different purposes from the exploitation of results perspective. A permanent bound links the IFC BIM1 models of the pilot sites to the GIS coordinates, allowing the new construction to be represented in 3D GIS mapping.
680676 [49]	For the building-related information, the IFC standard could represent the necessary information required by the District Data Model. Different alternatives to implement the BIM repository have been considered. For the district data, CityGML is the most widely adopted data model to represent a city in 3D including the geometric and semantic information. The district data will be stored in XML-like files or geospatial databases. For the contextual data, each domain of data will require a specific ontology.	Reinforcement of the presence of all involved stakeholders through an Integrated Project Delivery approach that will allow them to be articulated through a collaborative and value-based process to deliver high-quality outcomes.	The deliverable District Data Model” (DDM) integrated all the data required to perform a refurbishment analysis of buildings within a district, including Building data, District data, Contextual data, DPIs, Intermediate results, targets, barriers, and boundaries. The DDM played a crucial role in ensuring the interoperability between standard data models. It facilitates the intertwining of standard data models with domain-specific ontologies.
681673 [50]	The project used a combination of old photographs, 3D surveys, satellite images, and digital data elaborations, from which the archaeologist could indeed retrieve fresh information.	Not identified	Not identified
775971 [51]	Five state-of-the-art prototypes for both greenfield and brownfield exploration sites were intended, involving the use of UAV and helicopter-based geophysical systems, electrically-driven seismic vibrators, prototypes for in-mine and slimhole geophysical surveys as well as developing new algorithms for better handling and modelling of legacy and modern geophysical and geological data.	Not identified	Smart Exploration was committed to making data generated within the project findable, accessible, interoperable, and reusable (FAIR), following the guidelines and instructions provided by the European Commission.

summarizes the evidence from the R&D projects of Table 3.

In summary, there is clear evidence that recent R&D projects related to the selected ERA challenges pay attention to interoperability management and other systems engineering processes, answering the first research question (Q1). GIS, AR/VR, UAV, SCADA, and GPS are KETs used in the identified R&D projects. A similar assessment should be conducted with other project databases to reinforce this conclusion.

Since none of the projects consider resilience, the second research question (Q2) is still open, and research is still necessary, as expected, because building resilience is a relatively new topic.

4. An Integrated Cost Model for Private Investment and GIS

Using KETs to build resilience requires investment, which can be private or public. To get a return on their investment, it makes sense to model the economics from the perspective of the mineral water sector, but this has to be performed site by site to estimate risk, cost, and savings or opportunities. Similar sites with mineral water factories have been selected (Site 1, Site 2, and Site 3). Site 1 was chosen to start modelling economics.

4.1. Economic Model Description

The authors suggest an annual integrated cost model by adding the cost of the risk and the operational cost from the private perspective:

• The impact of the fire risk on the yearly water production is estimated in 10 years of the annual average sales for the fraction of the production affected by fires. This fraction is calculated as the “Woody Surface in the Watershed/Watershed Surface” ratio by the “Annual Production/Precipitation” ratio. The risk probability was estimated in Section 1.1. Multiplying the impact and the probability provides a cost of the risk. This risk can be reduced proportionally as long as the risk management solution covers the whole woody surface.
• The annual operational cost of the risk management solution is the sum of the “IoT or Satellite rent or use” to provide early detection of forest fires and the “Control central: staff, amortization, maintenance,” all for a certain covered woody surface. The first cost depends on the forest surface to protect, but the second cost does not. Considering the public administration’s involvement, reducing this cost by 50% is possible. The estimated lifespan of the assets is 20 years.

In summary, the dominant parameters of the basic economic model are the forest surface to protect, the probability of yearly forest fires, the water cycle balance, and the average annual plant production. The last two parameters are available in the AWS reports, but the first two need other data sources.

4.2. Economic Model Results

The first results of the integrated cost applied to a specific site (Site 1) show an integrated cost of 0.7% of the yearly average sales to minimize the risk cost of a 3.2% production loss with the current fire protection defined by the amount of forest fire vigilance stations and the IoT-Satellite solution. Of course, 0.7% is not far from the average yearly net private investment in R&D in Spain, assuming public subsidies of circa 50% and the correspondence between the Gross Domestic Product (GDP) and the annual average sales of the concerned firm. If the cost uncertainties are greater than expected, the mineral water sector may be receptive to such investments [52,53].

4.3. Replicability Basics with GIS and Interoperability Management

The European Commission publishes a list of natural mineral and spring waters in the Official Journal of the European Union according to Article 1 of Directive 2009/54/EC. In the case of Spain, 162 sites can be found, 2 of which have been certified by the AWS certification scheme. A sample of 27 mineral water plants next to forest areas was generated with Google Maps™, Mountain View, CA, USA. In 17 of them, 2 near water masses (river and reservoir) exist, showing an interesting connection for the industry to contribute to water movements to secure a certain reservoir level for forest fire fighting purposes. This reduces the sample size for early but wide-scoped exploratory purposes. This represents around 10% of the 162 listed sites.

MITECO offers free access to GIS layers to estimate the woody surface of a watershed and yearly precipitation estimated with meteorological data from AEMET’s public services [54].

To explore the possibility of conducting a rough but quick replicability analysis using GIS and verification techniques, the authors carried out a first similarity analysis of the mineral water plants in Southern Europe with three ratios to estimate the error of earth parameters for the minimum sample size of 17 sites. The data sources were two (2) AWS reports and Google Earth™ data (orthophotos and digital model terrain).

Table 5. Replicability analysis using GIS.

Parameter	Site 1	Site 2	Site 3	Average	Std. Deviation	Estimated Sample Average Error Band 95%/5%
Annual Production/Precipitation	1.9	2.6	2.5	2.3	0.31	0.15
Woody Surface in the Watershed/Watershed Surface	0.5	1.0	0.4	0.63	0.26	0.13

shows the results.

A 20% estimated error (0.13/0.63) is acceptable for the integrated cost accuracy but defines a limit on the investment cost uncertainties. For this reason, it is worth introducing the energy storage cost and opportunities (for instance, based on water pumping and electric batteries) to secure a feasible economy at any of the 17 sites, answering the third research question (Q3).

It is worth noting that one of Site 1’s parameter values is inside the 95%/5% error band for the sample average, which makes this site interesting for further technology pilots parallel to GIS and verification techniques.

The need for resilience depends on the magnitude of the risk. Unless the market accepts being involved in reducing risk, the research question (Q4) is still open, and further research is still necessary.

4.4. Methods and Materials

At this point, two questions are still open. Conducting profound research in an extensive scientific and technology database and using a methodology are necessary. ScienceDirect™, Amsterdam, The Netherlands (Search Portal) was chosen for this. Figure 2 summarizes the methodology followed in Section 5 and Section 6.

5. The R-ISSUES Model

At this point, the authors introduce a conceptual model: “Rural-Interoperable System of Systems for Unified Environmental Stewardship”. A concise explanation of the model is as follows:

• It is rural because it intends to support digital rural jobs in different sites and similar businesses related to biodiversity conservation.
• It is interoperable, as it uses the documented information model RSHP [62], which is compatible with formalization standards according to this reference, OWL2, and RDF, promoting semantic interoperability.
• It is for a system of systems (SoS), as the model provides a common understanding of the systems engineering (SE) discipline for different coexisting organizations.

Since the model aims to unify concepts along the lifecycle of environmental stewardship solutions (systems), it is represented in an ontology using a representation language for knowledge elements and relationships between those elements and attributes (such as assumptions or reasonings). This allows for semantic clusters and textual patterns (using semantic clusters) within the same knowledge base in RSHP [63]. This is remarkably interesting because the same ontology can be used for searching textual evidence within any research document, saving time, as demonstrated in [43], and, further, in the engineering lifecycle with specific systems-engineering-enabling tools [32]. It is possible to reuse this ontology by using tools compatible with the RSHP specification, with particular Application Programming Interfaces (APIs) or just by transferring the semantic relationships to own-made software or purchasing service menus because the ontology’s elements presented in

Table 6. Ontology, bibliography search criteria, and Cluster extension for Textual Alerts.

Concept	Model Element	Type of Element	Metadata and Operators (for the Search Portal)	Keywords (for the Search Portal)	<Cluster extension> (for Textual Alerts)
FIRE	Forest Fire	Risk	Fire AND business	Risk, forest
BUSINESS	Mineral and spring water industrial business	Agent		Mineral water, spring water, business, firm	Investment, asset, upfront cost
STAKEHOLDERS	Stakeholders	Agent		Public, agency, council
CERTIFICATION	Water Stewardship certification	Asset		Certification, stewardship
DEMONSTRATION	Demonstrate responsibility and resilience to water risk	Process		Assessment, verification, demonstration
DETECTION SYSTEM	Early fire detection system	System		Detection	Sensor, camera, Infrared, IR, satellite
W-STORAGE SYSTEM	Water storage system for extinction	System		Water storage, water reservoir	Pump, damp, pool
E-STORAGE SYSTEM	Electricity storage system	System		Battery, pumping, electricity storage, fuel cell	Energy storage
RESILIENCE FRAMEWORK	Resilience Engineering Framework	Enabler	Engineering AND resilience	Resilience framework, taxonomy, ontology	Recovery, resilience
SOS	System of Systems Engineering approach/es	Enabler		System of Systems, SoS
BIM	Building Information Modelling	ICT		BIM, Building Information Modelling	Geometry, 3D, materials
BIM OPEN DATA	Open data for Interoperability (IFC)	Data		Open data, IFC
INTEROPERABILITY	Systems Engineering Interoperability ICTs	ICT		Interoperability, digital threat, ICT, semantic

are pretty intuitive. This is because the first use of ontology is to provide some semantic interoperability (and common understanding) between the researchers and the scientific and technological community.

A specific use case of the R-ISSUES model’s ontology is to detect research trends, providing a first set of readings for Q2 and Q4, which must be converted into textual alerts in documents obtained from search portals. Table 6 provides the terms (Concept), a short definition (Model Element), an attribute (Type of element), and a semantic cluster composed of the same keywords (for the bibliography Search Portal) and the list called Cluster extension (for Textual Alerts).

After searching for research papers in the ScienceDirect™ portal, 154 research works were found, 48 of them under the scope and covering at least one concept.

Table 7. Results of the bibliography search using the ontology.

Fire	Stakeholders	Demonstration	Detection Systems	Resilience Framework	SoS	BIM	Interoperability
12	4	1	5	8	12	5	1

shows the results.

The authors consider the following readings as relevant “per se”, without the need to apply a textual search inside the documents.

Table 8. Readings for the model improvement regarding forest fire.

id(1)	Readings about Forest Fire	Improvements to the Model
1	https://doi.org/10.1016/j.jenvman.2013.08.033. Forest fire management to avoid unintended consequences: A case study of Portugal using system dynamics [55]	Consider the resilience contribution of managing the forest.
2	https://doi.org/10.1016/j.jenvman.2022.116134. Trajectories of wildfire behaviour under climate change. Can forest management mitigate the increasing hazard? [56]	Consider the risk of managing the forest.
3	https://doi.org/10.1016/j.ress.2023.109588. Evaluating the resilience of electrical power line outages caused by wildfires [57]	Consider the power networks close to the industry to invest in resilience together.

,

Table 9. Readings for the model improvement regarding resilience engineering and SoS.

id(2)	Readings about Resilience Engineering and System of Systems	Improvements to the Model
1	https://doi.org/10.1016/j.procir.2015.08.001. Strategic Resilience for Through-life Engineering Services [58]	Consider resilience engineering services to manage complexity.
2	https://doi.org/10.1016/j.procs.2019.05.050. Engineering Resilience in Multi-UAV Systems [59]	Consider common challenges in resilience engineering with Unmanned Aerial Vehicles (UAVs)

and

Table 10. Readings for the model improvement regarding BIM.

id(3)	Readings about BIM	Improvements to the Model
1	https://doi.org/10.1016/j.procir.2021.01.124. The Augmented Reality Technology as Enabler for the Digitization of Industrial Business Processes: Case Studies [60]	Consider the common Augmented Reality (AR) technologies to address the industry challenges.
4	https://doi.org/10.1016/j.apergo.2020.103154. “Integrated modelling of built environment and functional requirements: Implications for resilience” [61]	Consider requirement management for resilience as a complement to the construction requirements in BIM.

summarize these references and remark on potential improvements for the R-ISSUES model but not specifically the questions (Q).

As can be seen in Table 7, BUSINESS, CERTIFICATION, W-STORAGE, and E-STORAGE are missing concepts but also BIM OPEN DATA; nevertheless, to answer the research questions, all the documents are analysed by making use of a Natural Language Processing method, as explained in [43], and the Cluster of Table 6 for extending the Filter Clusters.

Table 11. Textual alert composition with extended clusters.

Question	Filter Cluster	Context Cluster	[Pattern 1]	[Pattern 2]
(Q2) Is BIM being used for building resilience?	<BIM’>	Cluster 1: <RESILIENCE FRAMEWORKS’>	<Filter>…<Cluster 1>	The reverse of pattern 1
(Q4) Is investing in resilience in the investment scope of private firms?	<BUSINESS’>	Cluster 1: <DETECTION SYSTEM’> Cluster 2: <W-STORAGE SYSTEM’> Cluster 3: <E-STORAGE SYSTEM’>	<Filter>…<Cluster 1> OR <Cluster 2> OR <Cluster 3>	The reverse of pattern 1

provides the composition of the textual alerts.

6. Results and Discussion

After applying the textual alerts and the expert criteria to the 48 readings,

Table 12. Textual alert results and expert’s utility validation (R = Relevant, I = Interesting, IR = Irrelevant).

id(4)	Bibliography Index (DOI) and Title	Expert’s Utility Validation
		Q2	Q4
1	https://doi.org/10.1016/j.envdev.2024.100971. Community-based fire prevention and peatland restoration in Indonesia: A participatory action research approach.		IR
2	https://doi.org/10.1016/j.future.2015.08.004. Smart cyber society: Integration of capillary devices with high usability based on Cyber-Physical System		I
3	https://doi.org/10.1016/j.wasman.2024.04.030. Early detection of deep-seated smouldering fires in wood waste storage using ERT		R
4	https://doi.org/10.1016/j.jii.2023.100445. Digital twins for performance management in the built environment	I
5	https://doi.org/10.1016/j.ssci.2019.104556. Extended FRAM model based on cellular automaton to clarify the complexity of socio-technical systems and improve their safety		I
6	https://doi.org/10.1016/j.eng.2019.01.014. Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison	IR
7	https://doi.org/10.1016/j.procs.2016.09.302. A Network-Based Cost Comparison of Resilient and Robust System-of-Systems		I
8	https://doi.org/10.1016/j.ress.2016.08.013. A quantitative method for assessing the resilience of interdependent infrastructures		I
9	https://doi.org/10.1016/j.dibe.2024.100466. A perfect storm: Digital twins, cybersecurity, and general contracting firms		IR

provides the results.

According to Table 12, several relevant or interesting readings demonstrate that the fundamental ontology can detect trends in more than 27% of cases. These results are included in

Table 13. Analysis of relevant and interesting readings after textual alert use.

id(4)	Readings	Potential Trends
3	https://doi.org/10.1016/j.wasman.2024.04.030. Early detection of deep-seated smouldering fires in wood waste storage using ERT [64]	New techniques to detect deep-seated smouldering fires in combination with other fire detection techniques such as IR cameras, gas sensors, and video and satellite-based monitoring techniques.
4	https://doi.org/10.1016/j.jii.2023.100445. Digital twins for performance management in the built environment [65]	Digital twins open up new opportunities where all actors (in the construction and product manufacturing) chain can be integrated under the same digital framework.
5	https://doi.org/10.1016/j.ssci.2019.104556. Extended FRAM model based on cellular automaton to clarify the complexity of socio-technical systems and improve their safety [66]	Models to understand complex dynamics of systems to manage safety.
7	https://doi.org/10.1016/j.procs.2016.09.302. A Network-Based Cost Comparison of Resilient and Robust System-of-Systems [67]	Modern SoS operations are highly dependent upon connectivity for performance.
8	https://doi.org/10.1016/j.ress.2016.08.013. A quantitative method for assessing the resilience of interdependent infrastructures [68]	Quantitative methods aimed at the assessment of resilience in infrastructures, using specific metrics.

.

Regarding the questions, Q2 has one relevant or interesting reading from five related to BIM readings (20%), and Q4 has three relevant or interesting readings of five corresponding to DETECTION SYSTEMS (60%), with those R-ISSUES model’s elements being the ones generating readings. In the first approach, the Q2 answer is NOT IDENTIFIED YET, and Q4 is YES.

Since scientific and technological production is constantly growing, it is not possible to ensure that the answers will remain true for a long time. Adding more readings to this research may also change the figures.

7. Conclusions and Further Steps

The first conclusion is that the R-ISSUES model demonstrated usefulness in providing relevant information to the authors (experts), with 27% effectiveness in helping them answer 50% of the research questions covered in this work. The model is ready to enrol more interested experts and stakeholders in continuous model construction, utility validation, and application to real projects, starting from an R&D and innovation perspective.

Regarding the research questions (Q), some conclusions can be added:

(Q1)

Is BIM interoperating with other KETs? The answer is YES, as several R&D projects combine GIS and BIM. Still, it is also necessary to conduct further research and define new interoperability schemes.

(Q2)

Is BIM being used for building resilience? This work has not yet provided a conclusive answer. Still, it is supposed that public–private collaboration would impose similar requirements as a public purchase for the use of BIM.

(Q3)

How can the replicability of a set of solutions for resilience be assessed in practice? The answer is YES: modelling economics, identifying the key parameters, and using GIS tools to assess the potential interest and representative of a demonstration site. It is necessary to evaluate other opportunities’ replicability, such as those related to energy and water storage, just to be more conclusive.

(Q4)

Is investing in resilience in the investment scope of private firms? The answer is YES; it is a trend, as demonstrated by recent research.

Our suggested next step is to activate collaborative vocabulary enrichment and control activities and to include quality assurance automation. After that, it would be possible to transfer the model to public ontology in RDFs SKOS or OWL2 formats. As mentioned in the Discussion, more studies from the Scopus™ database on interdisciplinary research for model construction should also be reviewed. This requires expanding the R-ISSSUES model and answering the generic research question before closing the KET selection. What if we combine other industrial businesses next to the mineral water plants to create more quality jobs in rural areas? For this, the authors can follow other bibliometric assessments for IoT providing specific metrics [69,70] and start using the Object-Process Methodology (OPM, ISO 19450) [71] and framework for promoting interoperability and collaboration for SoS in the presence of sources of risk [72].