Technological Innovations for Enhancing Disaster Resilience in Smart Cities: A Comprehensive Urban Scholar’s Analysis

Abstract

Despite advancements, Smart Cities encounter hazards. Smart Cities’ higher reliance on interconnected systems and networks makes them susceptible to risks beyond conventional ones, leading to cascading effects. Hence, the effective use of technological innovations is vital. This effective use involves understanding the existing use of technology innovations for resilience making in Smart Cities and the wise utilisation of them as suitable for different contexts. However, there is a research gap for a fundamental study that synthesises the emerging and disruptive technologies that are being used to improve the disaster resilience in Smart Cities and how they can be classified. Therefore, this research aimed to address that need, so that a Smart City evaluating the technologies/tools for disaster resilience could wisely utilise the available resources and prioritise the most suitable for their context-specific needs. Following a comprehensive literature review, the study identified 24 technologies and/or tools for creating, sustaining, and enhancing the resilience within Smart Cities. In doing so, they should collect and manage citywide geodata and foster public participation. While the wise utilisation of the most suitable and feasible tools and technologies is a measure of smartness in a Smart City, the findings suggested four key factors with which these technologies could be assessed. These four factors included impact on society, the adoption speed by Smart Cities, the maturity of the technology, and the capabilities offered to the community.

1. Introduction

According to the United Nations [1], approximately three in five cities worldwide with a minimum of 500,000 inhabitants, collectively about one third of the world’s population, are vulnerable to natural hazards alone. While it is the main cause of many disasters, this urban population explosion is continuing, with overwhelmed cities becoming more and more vulnerable. Therefore, the world is in search of solutions to mitigate the impact of urban population growth while transforming its negativity into opportunities; as a result, several city conceptualisation models have been developed. Smart Cities are one of such city conceptualisations that were developed to address a number of unprecedented, interconnected, and complex urban challenges provoked as a result of rapid population growth [2]. Although Smart Cities have shown a promising future, offering solutions and opportunities to the urban community, disasters still affect Smart Cities, and they can wipe off years of development in no time. Disasters affect Smart Cities differently and sometimes more severely given their unique ecosystem [3], for example, cyber-attacks [4]. Demonstrating a tragic example of how the ripple effect of a catastrophe affects a Smart City, Wuhan, a modern Smart City with more than 11 million people in China, was closed due to the COVID-19 pandemic [5]. As one of the most severe devastations the world has ever combatted, the COVID-19 pandemic’s aftermath highlighted the importance of resilience in response to any kind of disaster [6]. As a result of the growing vulnerability of urban ecosystems, including Smart Cities and the rising awareness of disaster risks, making Smart Cities disaster resilient is not only a timely need, but should be a natural extension of accelerated urban development.

According to Samarakkody et al. [7], disaster resilience in Smart Cities can be understood as restoring or maintaining an equilibrium that refers to the state of balance in the Smart City’s domains, including smart environment/ecosystem(natural environment), smart people/people, smart governance/leadership and strategy (knowledge), smart economy/economy, smart living and smart mobility (transportation)/infrastructure, spatiality, society and living (organisation), and ICT infrastructure, in the face of disruptions and stressors. Disaster resilience involves extra work than what is given for disaster risk management, and is the guiding principle for urban development, emergency/crisis management, and disaster risk management [8]. Smart Cities being more advanced gives them the ability to improve their disaster resilience, primarily by responding to hazards quicker [9]. Although technologies cannot stop disasters from taking place, they can be very useful for disaster preparedness, especially including prediction, early warning, and rescue operations post-disaster [10], which eventually rewards the city to operationalise its resilience mechanism. For instance, having well-defined early warning and post-disaster management systems in a city as part of its disaster risk management strategy/mechanism enables these cities to return to their state of equilibrium faster following an extreme event [11]. Hence, utilising the potential benefits of technology and innovations is of the utmost importance in creating resilience in Smart Cities.

Over the last decades, there has been a significant improvement in cities adopting the Smart City vision and utilising technology solutions for effectively managing hazards and enhancing resilience. One example includes security and emergency management through an integrated facility called Orlando Operations Center, which operates by employing features such as city monitoring through display screen walls, remote CCTV management, transit and evacuation management using EMD and CAD software and AVL technology, and alert and other interdepartmental communication services, etc. [12]. Vulnerable cities following destruction have been planned to be reconstructed as Smart Cities. For example, the reconstruction plan of the Taro district in Japan, which includes the relocation of safe, guaranteed residential zones, highly elevated transportation networks, evacuation towers, and solar energy power generation, etc. [13]. A few other Smart City case studies in Japan have deployed technological innovations, for instance, Kyoto City (with the establishment of the Kyoto Big Data Utilization Platform), where next-generation digital signage, environmental sensors, and smart lights were installed to collect human preference, flow, and environmental data, and Yokohama city, which aims to strengthen its disaster prevention capabilities with renewable energy usage to transmit emergency information while securing energy during disasters [14].

Successful Smart City projects call for two crucial competencies: (1) an understanding of the potential of technology solutions in the context of urban systems, and (2) integrating technology solutions rather than applying them in silos [15]. This brings attention to the gap between real city problems and the technological solutions pioneered by Smart City proposals [3]. This implies that the tools and technologies that are allegedly designed to address city problems, including disaster resilience, either oversee the real problems in a Smart City or are not strategically being applied with the correct understanding of the potential of the technology solution. Therefore, it can be argued that ill-chosen technologies applied in silos are a prominent bottleneck for effectively harnessing the benefits of technological innovations that can be used to make Smart Cities disaster resilient. In fact, according to Bellini and Nesi [16], the application of Smart City solutions is often siloed and needs to be approached holistically to coherently drive the strategy and resources towards a well-defined goal such as city resilience. For example, integrating data from social media, sensors, and other sources to increase situational awareness and response. Therefore, it is important to understand the technologies’ fit for the purpose of different Smart Cities that intend to be resilient, and then determine the right mix of technologies.

Despite the research and industry need for a fundamental study to comprehensively synthesise the prevailing technological solutions for disaster resilience in Smart Cities, existing studies are only looking at individual technologies. While those in-detail studies can be of great use for fully understanding a technological solution, their depth of information should be simplified to construct new insights through a holistic synthesis. Hence, this study first identified the existing technological solutions, as a fundamental study that could well be the basis for future studies that intend to provide different other meanings to groups of these technologies. Bearing in mind the limitations of generalising technologies within the Smart City scenario, it is important to have an assessment tool that any Smart City could employ in understanding the impact of the technological solutions available to them. It can be argued that this classification insight is a main contribution to the knowledge base, as there has been no previous study conducted to classify the technologies in a Smart City based on their impact, let alone in urban disaster-resilient applications. This study reflects on several linkages between theory, innovations, society, disaster resilience, and Smart Cities, ultimately deriving a new theory. Therefore, this study makes a niche contribution to both the industry and literature by introducing a new concept for classifying technological solutions.

While there is a range of technologies that are being researched and are in use for disaster resilience, it is up to a Smart City to validate and select what is best suited for them. In support of this judgement to be made by individual cities, this study aims to investigate the potential of emerging and disruptive technologies that are in current use (or being researched) for improving the disaster resilience in Smart Cities. In order to investigate the potential of these technologies, they will be first identified using a systematic literature review and then classified. Classification is important, mainly to sort technologies based on their impact made towards creating, enhancing, and sustaining the disaster resilience in Smart Cities. Their impact could be understood by different means, for instance, their impact on society. In fact, as aforementioned, there is a clear research need for interdisciplinary research that helps to integrate technology with social sciences. In this context, this study aims to investigate emerging and disruptive technologies for improving the disaster resilience in Smart Cities by the means of understanding the impact of these technologies on society.

2. Methods

In exploring this potential, initially, a comprehensive literature review was undertaken to identify the emerging and disruptive technologies and tools that improve the disaster resilience in Smart Cities. It can be argued that a comprehensive literature review is more appropriate than a systematic literature review for this research, as the scope of the review is broad and the data set is unmanageable. The said data set in this stage, which is the tools and technologies within the fields of disaster resilience and Smart Cities, was researched individually in previous studies, but this study is the first to list out the tools and technologies in the field of disaster resilience in Smart Cities after reviewing scattered studies. Hence, to set a basis for the search of the literature on technologies and tools (that improve the disaster resilience in Smart Cities), Stratigea et al. [17]’s study was used. Stratigea, Papadopoulou, and Panagiotopoulou [17] identified the tools and technologies commonly applied in Smart Cities under 3 categories, as follows:

• technologies and tools for citywide geodata collection and management (cloud computing, sensor networks, location-based services, geo-visualization, Geographic Information Systems, mapping, the Internet of Things (IoT), and data warehouses, etc.)
• technologies and tools for public participation (crowdsourcing platforms, web-based participatory tools, social media, and Living Labs, etc.), and
• sectoral applications (for example, energy, transport, and environment, etc.)

Building upon the findings of Stratigea, Papadopoulou, and Panagiotopoulou [17], this study searched the literature on the sectoral application of ‘disaster resilience’ under the first 2 categories. Thereafter, to assess the potential of the identified tools and technologies for transforming Smart Cities’ resilience, the findings were classified under 4 key criteria that determine the impact of the identified tools and technologies towards achieving disaster resilience.

Table 1. Factors/criteria to assess the potential of emerging and disruptive technologies for improving disaster resilience in Smart Cities.

Factor/Criteria	a	b	c	d	e	f	g	h	i
Impact on society	✓	✓	✓	✓	✓	✓	✓	✓	✓
Adoption speed by Smart Cities	✓	✓	✓	✓	✓	✓	✓	✓	✓
Maturity of the technology	✓	✓	✓		✓	✓	✓	✓
Capabilities offered to the community	✓	✓	✓	✓	✓	✓	✓	✓	✓

a = [17], b = [18], c = [19], d = [20], e = [21], f = [22], g = [23], h = [24], and i = [25].

is a synthesis of the key studies that indicates the factors that can be used to assess the potential of these tools and technologies applied to Smart Cities.

The above table depicts 4 factors under which emerging and disruptive technologies in Smart Cities can be classified to assess their potential. This mainly represents the essential link between technology and society. Given society’s prominent role in creating resilience, it can be argued that these 4 factors are more than justifiable for assessing the potential of emerging and disruptive technologies in Smart Cities that help to improve disaster resilience.

3. Results and Discussion

This section is organised under two sub-sections: (1) emerging and disruptive technologies for improving the disaster resilience in Smart Cities, and (2) the classification of these technologies under the four factors identified to assess the potential of emerging and disruptive technologies in Smart Cities that help to improve disaster resilience.

3.1. Emerging and Disruptive Technologies for Improving Disaster Resilience in Smart Cities

All research on Smart Cities is based on the idea that ICTs, the Internet, digital tools, and advanced technology discoveries leverage the smartness of a Smart City [26]. Hence, understanding the technology solutions and tools that can make cities disaster resilient is one way of evaluating where a Smart City stands in terms of the technology resources to expedite achieving, enhancing, and sustaining its resilience. While the below lists of technologies provide an idea of the technologies that are being used in other Smart Cities or researched, an individual Smart City should assess what technologies best solve their current issues related to disaster resilience, or what technologies best suit their Smart City (as Smart Cities are context-specific).

3.1.1. Technologies and Tools for Citywide Geodata Collection and Management

Geodata (also known as geospatial data) are the spatially referenced data or data that have a geographic or spatial component [27]. Geo-spatial data encompass a broad spectrum of data sets and formats, and their versatility make them applicable to solving a wide range of tasks associated with all phases of disaster resilience management [28]. Citywide geodata collection and management is vital for disaster resilience, as it provides cities with valuable insights so that the cities could better prepare for, respond to, and recover from disasters. Below is a list of identified tools and technologies that are important for citywide geodata collection and management.

• Cloud computing

Cloud computing is a computing technique that delivers various IT services with low-cost computing units connected by IP networks, so that users from anywhere with an internet connection can access these IT services on-demand, without needing to install any hardware or software on their local devices [29,30]. As the cloud offers virtually unlimited resources by the means of computing power and storage, geodata- and Web-GIS applications naturally take advantage of cloud computing technologies [31]. Cloud computing provides the infrastructure and platform for enabling the digital technologies that Smart Cities employ to improve the quality of life of their citizens, increase efficiency, and enhance sustainability [32]; its potential to revolutionise the Smart City landscape helps to make these cities disaster resilient.

During disaster situations, cloud computing enables data and computation (software being used and algorithms, etc.) to be saved (time snapshot) and relocated to another (safer) physical location swiftly, and this includes system backup [33]. Large amounts of disaster-related data collected from different sources such as sensors, satellite imagery, and social media, etc. can be stored and analysed, and this helps cities to pre-identify hazards and disaster risks and prepare [34]. Cloud computing also offers concurrent access to the cloud, which may help to increase community engagement [35]. Most importantly, cloud computing serves as an underlying infrastructure that provides on-demand computing resources to meet the dynamic computing requirements of real-time disaster/hazard analysis, emergency response, and disaster coordination [36]. Cloud computing also offers a finer solution for disaster modelling and simulation [37] and eventually helps Smart City infrastructure to identify existing vulnerabilities, so that resilient infrastructure can be better designed/improved. However, during actual disaster scenarios, where power, electricity, and communication infrastructure are broken down, the use of cloud services becomes a challenge [34]. To overcome the challenges of using cloud services during disasters, the involvement of evolving disruptive technologies, including fog and edge computing and network work, is recommended [10,34]. According to the researchers Ujjwal, Saurabh, James, Jagannath, and Nicholas [34] these technologies act as a transitional data delay for the cloud’s further assessment. Further, some studies have also looked at the resiliency techniques in cloud computing infrastructures and applications [38].

• Internet of Things

The Internet of Things (IoT) is a term used to describe the connection of physical objects such as mobile devices, sensors, buildings, and vehicles, etc. to the Internet, allowing them to collect and exchange data with each other [39]. Some IoT implementations feature smart roads, smart grids, smart parking, tank levels, traffic congestion, smartphone detection, radiation levels, smart product management, landslide and avalanche prevention, and snow level monitoring, etc. [40]. With the development of Smart City solutions that employ the IoT, the world has been offered more factual knowledge about urban systems with high spatial and temporal resolutions [41]. The density and coverage of IoT devices and their relatively low energy consumption allow for large, self-organising networks in emergency communication for longer periods [42]. IoT-enabled disaster management systems that incorporate evolving data analytics and artificial intelligence tools can be used as early warning mechanisms and for finding victims and implementing possible rescue operations [43].

• Bigdata

The literature refers to Bigdata using two different defining characteristics: (1) the massiveness of the data, and (2) complementing techniques and evolving technologies that are vital for the effective processing and conducting of an insightful analysis of massive volumes of data, in a way that their hidden values can be discovered [44,45]. With advanced Big Data Analytics, large disaster-related datasets from multiple sources can be examined in real time during all phases of disaster management (including preparedness, mitigation, response, and recovery) to extract valuable information that can help make informed decisions during the resilience journey [44]. Multidimensional big data analytics, including descriptive, prescriptive, predictive, and discursive analytics, helps to create and enhance resilience in the aforementioned phases of disaster management, especially in restoring normal life following a disaster [46]. According to the authors Sarker, Peng, Yiran, and Shouse [46], descriptive analytics deal with the description of the status, condition, and criticality of disasters, while prescriptive analytics focus on management-policy-related issues for disaster resilience. Likewise, predictive analytics focus on inferences related to imperceptible issues that could influence future tasks, including early warning and forecasting, while discursive analytics deal with community-resilience-related aspects, such as raising awareness, timely response, and collecting feedback based on big data [46]. Hence, big data technologies improve the effectiveness and speed of the linkages between disaster information and the appropriate systemic responses of Smart Cities [47]. Compared to conventional cities, within Smart City contexts, rapid or real-time big data applications allow for a better mitigation and capacity enhancement to recover from extreme events relatively faster [48]. One example is the development of ‘geospatial big data’ from location-enabled mobile communication devices and other sensor network-based geospatial data acquisition systems; yet, due to their requirements, including high-speed internet connections, advanced network infrastructure, and knowledge of cloud-based computing, the use of cloud-based Big Data processing platforms is questionable in different Smart City contexts such as developing countries [49].

• Geo-visualisation and Geographical Information Systems (GIS)

Geo-visualisation includes modern digital ways of representing geospatial data and plays an important role in disaster modelling, scenario development, post-disaster analysis, and during the execution of search and rescue operations [11]. Geo-visualisation is often driven through Geographical Information Systems (GIS). GIS tools are useful for the production and presentation of the results obtained from spatial processing and analysis, which are ultimately used for better decision making [34]. GIS have been broadly used to produce hazard, risk, and vulnerability maps to effectively understand and manage the risks in cities [50]. In addition to risk assessments, GIS play an important role in emergency response and recovery and reconstruction, especially with their capability to analyse the real-time data from cameras and sensors [51]. GIS also assist in deploying location-based emergency services by facilitating the mapping of several contexts within the same area over a period of time, so that they helps to identify the environmental patterns/changes at local risk levels [52].

• Sensor networks

Sensor-connected buildings, critical infrastructure systems, and vehicles, etc., are critical in capturing real-time information about potential vulnerabilities before a catastrophic failure [53]. By using a combination of sensors, for example, seismic sensors, flood sensors, air quality sensors, weather sensors, thermal sensors, motion sensors, and radiation sensors, disaster responders can swiftly assess a scenario, identify the potential risks, and take suitable action to mitigate the larger impact [54]. According to Adeel et al. [55], Wireless Sensor Web (WSW) technology is useful for early warning and situational awareness to prepare communities and assets. Cheikhrouhou et al. [56] highlighted the synergistic combination of wireless sensor networks and 3D graphics technologies, where near-real-time 3D true-to-life scenarios can be generated based on the sensor data received from the real environment. Sharma et al. [57] explained the prominent use of low-power, low-data-rate wireless sensor networks (WSN) within the intelligent control system of Smart Cities, and Khalifeh et al. [58] highlighted the role of WSNs in securing the Smart City from various hazards.

• Grid technologies

A Smart Grid incorporates modern advanced technologies, intelligent algorithms, communication networks, and automation systems into the power system to enhance the system efficiency, reliability, resiliency, power quality, and cost effectiveness while providing customer with tools to manage their energy usage [59]. In the case of Smart Cities, innovations in smart energy systems and grids are capable of efficient energy consumption/generation, and hence are a popular choice, especially when renewable energy such as solar and wind energy are integrated [60]. Smart grid technologies can enhance a city’s resilience by reducing the length of power outages and consequently reducing the scale and severity of disaster impacts significantly [53]. Similarly, microgrids can improve post-disaster resilience significantly. With the ability to operate in ‘island mode’, microgrids continue to supply power in the event that the large grid is damaged during an extreme event, and they can be deployed rapidly [61]. Similar benefits for resilience could be gained with the use of smart water grids [60].

• Wireless Wide Area Communication and Wireless Local Area Networks

Wireless Wide Area communication (WWAN), for example Long-Term Evolution (LTE), Universal mobile telecommunication system (UMTS), Satellite Cellular, High-Speed Downlink Packet Access (HSDPA), or Wireless Local Area Networks (WLAN), for example, Wi-Fi and Bluetooth, etc., facilities interconnect a large number of heterogeneous mobile smart sensing devices, which allows for providing crisis management services, ranging from first responder localisation to all on-site activities within a smart city area [62]. Out of the above WWAN mechanisms, LTE/4G networks have the ability to provide technology agnosticism and provider independence, which is important for mitigating disruptions or outages in any one technology or operator [63]. On the other hand, satellite communication provides reliable communication services in remote or disaster-affected areas where terrestrial communication networks may be unavailable; hence, considering the strengths and weaknesses of both, research recommends the integration of satellite and LTE for disaster recovery [64]. Over the last decade, innovations in communication technology have played a crucial role in terms of ensuring the error-free connectivity in Smart Cities amidst major challenges as a result of the coexistence of a high number of intelligent devices [65].

• Location-Based Services (LBS)

The term location-based services (LBS) is interchangeably used with location services, wireless location services, mobile location-based services, location-enabled services, and location-sensitive services, referring to an innovative technology that provides information based on geographical location (of the user) [66]. The kinds of location-based technologies that offer consumer data services based on the position of the user are mainly used in emergency and rescue services, navigation and tracking, and public alerting and warning [67]. Supported by wireless communication technology, LBS technology has two approaches: 1) the location data are processed on a server and the result is sent to a mobile device, and 2) the location data can be used through an application on the mobile device [68].

• Geographical positioning techniques

The satellite-based Global Position System (GPS) is the first global location system in use and currently, location-based data can be obtained with one or more of many outdoor and/or indoor positioning determination technologies, classified as terminal/user-centric, network-centric, and hybrid solutions [52]. Assisted GPS (AGPS) is an improvement to conventional GPS, and was developed to compensate for the weakness of GPS. AGPS is a combination of mobile technology and GPS, where it makes use of local wireless networks for faster location acquisition than conventional GPS, with an enhanced accuracy [69]. Furthermore, satellite-based technology, the other popular technology, is network-based, and receives a signal from cell sites serving a mobile phone to determine location. Some popular methods include Angle Of Arrival (AOA), Time Of Arrival (TOA), Time Difference Of Arrival (TDOA), and hybrid methods [66]. Positioning, as a broad spatial computing area, has a large potential in Smart Cities as a means of helping to re-imagine, review, redesign, and compare alternative infrastructure futures to address risks [70].

• Blockchain

Smart-city-based applications necessitate transparent transactions (verified data/information stored), no single point of failure, data protection, and automatic decision making to ensure the authorization and integrity of transactions, and with an immutable decentralized ledger, blockchain technology serves that purpose in securing Smart Cities [71]. Blockchain technology is able to revolutionise disaster resilience, especially in managing the funding/aid to refugees [72]. Its smart contract functionality is highlighted in discussing the use of blockchain technology in disaster management, as a city’s disaster management policy can be scripted and damages can be logged, with costs being estimated early in the recovery process [73].

• Data Warehouses

A Data Warehouse (DW) is a database that stores an integrated and time-varying data collection derived from operational data and primarily used in strategic decision making [74]. The evolved concept of big data warehousing is more popular in the Smart City context, which supports fact-based decision making and has streaming and predictive capabilities [75]. Within the disaster resilience scenario, DWs can play an important role, as they consolidate and (ad hoc) analyse data from different sources (for example emergency response systems, sensors, and social media, etc.), while eliminating data redundancy for improved decision making [76].

• Digital twins

Digital twins facilitate comprehensive data exchange and contain simulations, models, and algorithms describing their counterpart (a physical asset, system, or process), including its characteristics and behaviour in the real world [77]. Urban Digital Twins (UDT) in Smart Cities help them to tackle urban complexities by visualizing the complex processes in urban systems and their dependencies, simulating possible impacts/outcomes, with a particular consideration of the heterogeneous requirements and needs of its citizens to enable collaborative and participatory planning [15]. Smart Cities with digital twins have the capability of synthesising the unique conditions and characteristics of a community during an extreme event and anticipating the evolution of that community following a disaster [78]. UDTs support decision making when planning activities are synchronised, in order to improve infrastructure system performance, lower planning conflicts, and the effective use of environmental and social resources [79]. A digital twin paradigm plays a significant role in a disaster-affected city, especially in terms of enhanced situation assessment and decision making, coordination, and resource allocation [80]. Linking every element in a city to a digital twin in the cloud allows for the better monitoring of performance and the detection of flaws [81].

• Unmanned Aerial Vehicle (UAV)

UAV path planning is envisioned to find the shortest and optimal path with the minimum energy consumption and optimal resource utilisation [82]. Drones have the capacity, responsiveness, and portability to increase cellular coverage and bandwidth for disaster relief efforts, and criminal surveillance, etc., and they are often considered as a timely solution during disaster rescue missions when regular wireless networks are disrupted [83]. Information about disaster-affected areas through aerial images from UAVs helps faster evacuations and the delivery of supplies through safe routes, even to inaccessible locations [84].

• Cyber-Physical Systems (CPS)

Cyber-Physical Systems simply integrate the physical element and computational element in engineered systems, where the sensors, actuators, and other devices are used to interact with the physical world and computer algorithms analyse and process data in real time [85]. There are only a few studies that have looked at the potential of a CPS for disaster resilience, which include those that studied CPS-based intelligent structural disaster prevention and reduction systems [86], emergency response [87], and pre-disaster response planning [88], etc. Smart cities can be viewed as a large-scale implementation of a CPS, where sensors monitor the physical and cyber components and actuators change the Smart City ecosystem environment [89]. However, while improving cyber infrastructure, CPSs can also introduce security vulnerabilities when the CPS is interfaced with a Smart City [90]. Infrastructure risk is one of the most discussed and critical risks in Smart Cities, given that the physical world and cyber world are integrated (Baker et al., 2019 [91]). Therefore, the CPSs of smart cities should be designed with balancing the cybersecurity capabilities and proactive intelligence against infrastructure risks and vulnerabilities [92].

• Building Information Modelling (BIM)

BIM is a technology that allows for the creation of a digital representation of the functional and physical characteristics of a building. Although this model is mainly used as a shared database for managing a building’s construction/facilities throughout its entire project lifecycle, it can provide a number of benefits for disaster resilience as well, especially in facilitating post-disaster damage assessment [93]. It also helps in generating and running simulations of the operation of the building facility and the behaviour of the occupants of that building, under both normal and emergency scenarios [94].

• Smart Disaster Response Systems (Smart DRS)

Compared to traditional disaster response systems, smart DRS deploy real-time data to respond in an efficient and timely manner [95]. Local communities, as the major component of a smart DRS, receive information from sensors and share it in order to obtain assistance [96].

• Early warning systems

Early warning systems are developed for a range of threats, including natural geophysical hazards, biological hazards, industrial hazards, complex socio-political events, human health concerns, and other threats within the urban disaster scenario. Many studies have been carried out on early warning systems, where real-time data are used to generate warnings for natural hazards [97]. Early warning systems developed for Smart Cities often incorporate different technologies such as Artificial Intelligence, IoT, and big data analytics to develop more reliable and resilient systems that are faster at prediction and detection [98].

• Virtual Reality (VR), Augmented Reality (AR), And Mixed Reality (MR)

Immersive technology is fundamentally a simulation of reality created by spatial, physical, and visual computers. Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) have the ability to change human–computer relationships with an immersive experience in a digital environment and make digital information meaningful and powerful [99]. With these technologies being able to capture the dynamic interactions in Smart Cities, the city can better prepare for hazards/disasters [100].

• Artificial Intelligence and machine learning

Artificial intelligence helps to reduce the cascading effects of the destruction of critical infrastructures and allows for rapid recovery [101]. Artificial intelligence (AI) applications, including tracking and mapping, remote sensing techniques, geospatial analyses, robotics, machine learning, drone technology, telecom and network services, smart city urban planning, accident and hot spot analyses, environmental impact analyses, and transportation planning, are the technological components of societal change, which drives the societal response to hazards and disasters [102]. Accordingly, machine learning and smart city planning are subsets of artificial intelligence. Studies have found prediction and forecasting, early warning systems, resilient infrastructure, financial instruments, and resilience planning to be the AI application areas in disaster resilience [103]. With the speed and better ability to analyse large volumes of disaster-related data (compared to humans), AI can generate acceptable forecasts to deploy resources and develop disaster plans [104].

The above reviewed are the most cited technologies and tools for the citywide geodata collection and management used for DRR and are linked with its potential applications in Smart Cities. While these technologies and tools range within a broad spectrum, there could be more that need to be researched further to justify their potential in creating disaster resilience within Smart Cities. The below section discusses the technologies and tools focused on public participation in creating disaster resilience within Smart Cities.

3.1.2. Technologies and Tools for Public Participation

• Crowdsourcing platforms

Crowdsourcing refers to data (by the means of ideas, content, services, or even funds) created by a large group of people as a response to an open call or invitation and is based on the underlying argument that a group can provide solutions to a problem more effectively than an expert [105,106]. Crowdsourcing applications can be of large importance in creating disaster resilience, as they acknowledge a variety of data collection forms, so that these broaden the information availability, especially during disasters to impacted communities and, at the same time, allow for the affected populations to communicate with the global community [35]. Data collected through crowdsourcing platforms also help Smart Cities to identify the health conditions of their victimised citizens following a disaster, utilise their resources by better understanding the extent of the disaster, position rescue teams, and help to minimise further damage to the environment/ecosystem [54]. Through active crisis crowdsourcing not only does the community receive a location-based warning and messages such as evacuation routes, but it also offers benefits to all stages of the disaster life cycle, as shown in Figure 1 [107].

• Volunteered Geographical Information (VGI)

Volunteered Geographic Information (VGI) is the creation of digital spatial data by groups of people reflecting on their views and geographical knowledge on the web [108]. Some refer to this as a subset of the crowdsourcing mechanism. According to VGI, information is more detailed, timely, and of a higher quality in many cases compared to what is provided by official institutions, but at the same time, the data quality and reliability are highly variable, undocumented, and at times incomplete [109,110], and these inconsistencies could lead to errors in disaster-related decision making and planning [111].

• Web-based participatory tools

Similar to crowdsourcing, web-based participatory tools allow the general community to share their ideas, thoughts, and views and collaborate over the internet. For example, the web-based participatory surveillance of infectious diseases collects real-time information on the distribution of influenza-like illness cases through web surveys [112]. Other similar examples include a web-based participative decision support platform where disaster experts, decision makers, and the community brainstorm risk mitigation alternatives and select the most appropriate from the proposed options [113], and participatory GIS applications that incorporate local knowledge into GIS where public access and collaborative mapping are promoted [114].

• Social media

Social media is a powerful and natural extension of the human sensory system; it includes not only disaster-related information shared by the general public, but also more trustworthy sources such as government authorities, research/academic institutions, and Non-Governmental Organisations (NGOs) [115]. Social media have primarily been used for disaster management to detect extreme events and hazards, and for emergency responders and relief coordinators to obtain situational awareness through social media users’ feedback and monitoring [116]. Researchers have also studied social-media(ted) crisis communication patterns to understand the behaviour of social media users (people and the community) during disasters, the findings of which could advise on taking resilient measures [117,118]. Data from social media are vastly important, as they overcome the data unavailability due to remote sensing data being absent during disasters when geo-temporal gaps take place as a result of satellite revisit time limitations, atmospheric opacity, or other obstructions [119].

• Living Labs

Living Labs are a user-centric innovation setting built on everyday research and practice, where stakeholders collaborate to design, test, and validate innovative technologies, solutions, and services [120]. Living Labs is a platform for constructing Smart City solutions including those aimed at disaster resilience [121]. They help to provide a real-world environment to collaboratively explore, design, test, and implement innovative solutions for disaster resilience [18].

According to Sakhardande et al. [122], the absence of integrated infrastructures and platforms that could facilitate data acquisition during emergency management results in shortfalls in the process. This leads to the need for integrating technology solutions holistically, rather than employing them in isolated silos. A Smart City’s potential to integrate new technologies has been widely argued, for example, by Anttiroiko et al. [123], and integrating different IoT ecosystems as Smart Cities’ main enabler plays a key role in them doing so [124]. Many scholars have looked at effective integration from several perspectives. For example, Soto et al. [125] discussed the potential of deploying the Smart City platform in a decentralised and federated way, enabling the interconnectivity of heterogeneous ICT systems of different entities and stakeholders. A similar approach was taken by Bonino et al. [126], and its first experimental application has also been carried out. Although the ideal integrated vision of a Smart City is capable of harmonizing these technologies, this has not been the case in many initiatives; hence, Barletta et al. [127] proposed an integrated model and smart program management approach, analysing the transversality and technological depth through their interdependencies. The importance of convergences between smart technologies has been justified by Singapore adopting smart technology integration, which is known as one of the smartest cities in the world [128]. Hence, addressing the links between the above technologies is vital.

Moreover, the above reviews on tools and technologies that facilitate public engagement suggest that there is a potential to build innovative yet inclusive technologies for all. While there are technologies and tools that serve the purpose of engaging society by different means, the above mentioned are the most common/mostly cited. The next section classifies the above-discussed technologies and tools based on different criteria.

3.2. Classification of Technologies

One of the major non-technical challenges for Smart Cities is the financial challenge, which can be discussed along the routes of limited funds, large up-front investments, the absence of a creative business model, and monetisation difficulties in SC investments [129]. Therefore, it is vital that Smart Cities utilise their limited resources wisely, and this involves choosing the tools and technologies for resilience that are the most suitable and feasible for their city. While it is not sensible to draw a generic list of the most suitable tools and technologies for disaster-resilient Smart Cities, as each Smart City is unique, scientific research can guide Smart Cities to make a judgement in this regard. Therefore, below is a discussion on how the potential of the tools and technologies for improving disaster resilience in Smart Cities can be assessed. The below discussion links innovation with theory and is structured under four sub topics (impact on society, the adoption speed by Smart Cities, the maturity of the technology, and the capabilities offered to the community), which provide a basis for understanding the impact of the technology.

3.2.1. Impact on the Society

Viewing a Smart City holistically as a system-of-systems is popular when conceptualising the Smart City notion, as it is a unit that encompasses a number of interconnected and complex systems, including the city’s environmental, economic, and socio-cultural systems [130,131]. Following along the same route, researchers have expounded people, structure, tasks, and technology as the key four systems that comprise a Smart City system, and eventually build a relationship between the elements of a Smart City and Leavitt’s System Model (also known as Leavitt’s Diamond), looking at the Smart City from the perspective of a socio-technical system [132]. Out of the four interacting components in Leavitt [133]’s model, technology represents the technical aspect of an organisation, while the people, structure, tasks, and the diamond structure showing the relationships imply the importance of treating the social and technical aspects as interdependent parts, as changes made to one element can have an impact on the others. Although the model and its extensions were initially focused on organisational change management, its use can also be found in Smart City research, especially as it overcomes the shortfalls of the standalone humancentric and technocentric modes of thinking, which are common to Smart City studies [132,134,135]. According to Mora et al. [136], with the socio-technical approach, the sociotechnical arrangements in a Smart City are envisioned to effectively deploy digital technologies to increase the ability of urban services to sustainably meet societal needs. The use of the social technical theory/approach to examine the effect of technologies on social practices, the organization of work, and society has been researched considerably [137]. Similarly, with a deeper understanding of the interdependencies between a city’s social and technical systems and how they influence one another, Smart City technologies could be classified based on their impact on social systems and even technical systems, or both. For instance, a technology that helps to coordinate emergency responders and enhance communication during extreme events could be classified under the technologies with a high impact on social systems. Below, Figure 2 depicts a matrix developed based on the impact on social and technical systems.

The tools and technologies identified in the previous section can be placed in the matrix based on their influence and impact on the Smart City’s social and technical system. Similarly, the tools and technologies can be classified based on their adoption speed, and this is discussed in the next section.

3.2.2. Adoption Speed by Smart Cities

There has been no consensus reached among practitioners or researchers regarding a standard list of technologies to be employed in Smart Cities. The fast winners or slow losers in technologies are context/user-specific. In assessing and predicting the variances in the adoption of these technologies, the Technology acceptance model (TAM) is widely used (Pichlak, 2016 [138]). The TAM postulates that the adoption of a technology is determined by its perceived usefulness to the user and perceived ease of use [139] (Dube et al., 2020). While the TAM looks at adoption from the user’s perspective, innovation diffusion theories are usually described from the technology inventor’s perspective [140] (Kopackova et al., 2022). Accordingly, they classify adopters (users of technology) based on their level of readiness to accept innovations (Bokhari and Myeong, 2022 [141]). Together, these theories help to categorise technologies based on their diffusion in Smart Cities. For instance, if digital twins are widely adopted for DRR in a selected group of cities, compared to Unmanned Aerial Vehicle (UAV)s, that technology would be understood as having a high level of diffusion. Similarly, diffusion could be assessed within a city as well. For instance, social media (considering its particular use in DRR activities) is widely adopted and has a large user base; hence, it can be classified as having a high level of diffusion. This could also provide an indication of the inclusivity of the technology and that its benefits are realised by all segments of society. Below, Figure 3 shows a framework developed incorporating the above-discussed factors.

3.2.3. Maturity of the Technology

According to Guseva et al. [142], the main restriction that hinders the full-scale development of Smart Cities is the expensive Smart City solutions, which are still at the introductory and approbation stages and not yet ready for scaling. Therefore, the maturity of technology is important when prioritising the resources within a Smart City, especially limited ones, which are allocated towards DRR. One of the widely used international assessment tools is the Technology Readiness Level (TRL) scale. This was initiated by the American National Aeronautics and Space Administration (NASA) to measure the maturity of space exploration technology, and later became an innovation policy tool of the European Union (EU) [143]. The scale depicts nine evolutionary stages that abstract how far a technology is from being ready for use in its anticipated operational environment [144]. The use of the scale is mainly for, but not limited to, making comparisons between diverse technologies based on their respective positioning on the scale, as well as monitoring the progress (usually over time) of an individual technology that involves a linear technology development process [145]. The application of the TRL scale within Smart Cities was researched once by Guseva, Kireev, Bochkarev, Kuznetsov, and Filippov [142], and it can be argued that, for a niche field like DRR technologies, its use can be highly significant in classifying technologies and understanding the maturity of the technology, to plan for its use in DRR activities. For instance, one may assume higher TRL levels as an indication of a higher potential impact on disaster resilience strategies/plans/activities. Technology readiness levels used by the EU/UKRI Science and Technology Facilities Council to determine whether a project/proposal is eligible for a specific funding opportunity can be listed as follows [143].

• TRL 1: Basic principles observed and reported
• TRL 2: Technology concept or application formulated
• TRL 3: Analytical and experimental critical function or characteristic proof-of-concept
• TRL 4: Technology basic validation in a laboratory environment
• TRL 5: Technology basic validation in a relevant environment
• TRL 6: Technology model or prototype demonstration in a relevant environment
• TRL 7: Technology prototype demonstration in an operational environment
• TRL 8: Actual technology completed and qualified through test and demonstration
• TRL 9: Actual technology qualified through successful mission operations.

With the use of the TRL, Smart Cities can assess the progress of different technologies in the TRL stages, and it provides them with an understanding of the potential risks and sometimes the extent of the reliability of the technology. For instance, some technologies may be associated with less risk when they are in the TRL 9 stage. Similarly, some technologies that need to be developed/validated in controlled laboratory environments or community-based pilot projects require extra facilitation, about which the Smart City should be carefully evaluated in terms of its resources and practices. In some cases, some technologies could be developed to suit better to a particular Smart City context with citizens’ feedback, and in such situations, it is better to select an alternative at a lower TRL stage. Likewise, the maturity of a technology, although not directly linked, helps to enhance the integration between technology and society.

3.2.4. Capabilities Offered to the Community

While much research on Smart Cities is monocentric towards technology, the authors of this study argue that people are as important as technology in a Smart City. According to Giffinger et al. [146], smart people are one of the core six dimensions in a Smart City, and they are not only an indication of the level of education/qualification, but also a collection of factors such as open mindedness, creativity, flexibility, and social interactions, etc. As the key to the creation of Smart Cities, technology should be conceptualised bringing the best out of its transformative’ dimension in a way that enables the capabilities of its citizens [147]. With regard to empowering users, growing scholarly attention has been directed towards integrating technology within the Capability Approach (CA) [148]. The notion of capabilities refers to fulfilling expectations and realising achievements, and as per the Nobel Laureate Amartya Sen’s CA theory, which emphasises individual capabilities and suggests that people are given opportunities to make choices on how to live their lives, they find valuable resources or commodities that individuals possess as providing a means to expand their capabilities. For instance, a study that developed a CA model to look at digital healthcare technology adoption by elderly people conceptualized independent living as a set of capabilities, expounding the freedom to live at home in the way that an elderly person wishes as being facilitated by digital technologies [149]. Similarly, within the broader perspective of the CA, this study argues technology to be conceptualised as a resource, while the disaster resilience of a Smart City (citizens’ living towards making the city disaster resilient) is a set of capabilities. This entails the enhanced living of individuals in a Smart City that intends to sustain disaster resilience with the right utilisation of technologies. Accordingly, there are technologies that become significant in empowering communities and individuals to build resilience within Smart Cities, for instance, Volunteered Geographical Information (VGI) and social media, which involve real-time information that is helpful for better responses during extreme events.

The findings can be supported with a few example case studies that deployed smart technologies to create resilience. The first factor, which highlights the impact on society, can be looked at considering different qualitative and quantitative dimensions. For instance, Saudi Arabia’s holy city of Mecca is a pilgrimage that attracts millions of people, and stampede such as that in 2015 could result in multiple fatalities. Crowd and crisis management measures before the 2015 incident had several shortfalls, but crowed security management solutions based on big data (which takes attributes such as crowd moving velocity field, density, and direction into consideration) that are being administered now are widely identified as a high-impact scenario [150]. This example also reflects on the next factor, i.e., the adoption speed, which looks at technology acceptance based on its perceived usefulness and ease of use. As part of the Mecca crowd management strategy, electronic identification bracelets were planned to be introduced for all pilgrims, which helped the authorities to identify people and track crowd routes [151]. As a wearable, its acceptance could be assured due to the other information the e-bracelet holds, which includes a prayer time alert and a compass for identifying the prayer direction [152]. While its maturity is not at its best, the capabilities offered to the pilgrim community are prominent, as its benefits go beyond preventing stampedes, for example, being useful in understanding the medical needs of pilgrims. Likewise, the technologies’ impact could be assessed considering all or several factors, depending on their suitability in certain scenarios.

The above-discussed four factors provide a measure of the potential of a technology/tool deployed to build/enhance/sustain the disaster resilience in Smart Cities. Potential in the Smart City circumstance is highly context-dependent, as one technology/tool that works best for one Smart City may not be the best solution for another Smart City with a different character. Evaluating the potential of tools and technologies usually involves prioritising or selecting the most suitable from a few alternatives. Together with feasibility studies, a Smart City that plans the technologies/tools to build/enhance/sustain its disaster resilience may carry out assessments for all the above four factors, make informed decisions, and ultimately prioritise the most suitable for them.

4. Conclusions

Smart Cities provide digitally enabled solutions to various urban problems; hence, they are unique to each other. In light of their limited resources, Smart Cities should wisely decide on the technologies that they should deploy to address their context-specific needs, as well as disaster resilience being a mandatory requirement. While there are studies that have discussed different emerging and disruptive tools/technologies for disaster resilience and tools/technologies for Smart Cities, there is an absence of the convergence of both aspects. Not only there is a research gap to identify the tools and technologies for improving the disaster resilience in Smart Cities, but it is also a clear research need to guide Smart Cities in understanding the potential of tools and technologies for improving the disaster resilience in Smart Cities. Therefore, this research addresses this research need and explores the potential of tools and technologies for improving the disaster resilience in Smart Cities by identifying the most researched tools and technologies for improving the disaster resilience in Smart Cities and providing a guide to evaluate their potential.

The literature findings on the tools and technologies for improving the disaster resilience in Smart Cities are organised under two categories: (1) the technologies and tools for citywide geodata collection and management, and (2) the technologies and tools for public participation. Citywide geodata collection and management are crucial for disaster resilience, as they could provide Smart Cities with valuable insights about the city ecosystem, enabling them to better prepare for, respond to, and recover from disasters. The tools and technologies that are important for citywide geodata collection and management in transforming Smart Cities to be resilient include cloud computing, the Internet of Things, Bigdata, Geo-visualisation and Geographical Information Systems (GIS), Sensor networks, Grid technologies, Wireless Wide Area communication and Wireless Local Area Networks, Location-Based Services (LBS), Geographical positioning techniques, Blockchain, Data Warehouses, Digital twins, Unmanned Aerial Vehicle (UAV), Cyber-Physical Systems (CPS), Building Information Modelling (BIM), Smart Disaster Response Systems (Smart DRS), Early warning systems, Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), Artificial Intelligence, and machine learning. The technologies and tools for public participation include crowdsourcing platforms, Volunteered Geographical Information (VGI), web-based participatory tools, social media, and Living Labs. The above categorisation is based on Stratigea et al.’s [17] study, which was complemented by the findings of similar studies. Those listed are the most researched/implemented combinations of technologies and the developments of these technologies have been presented in some product-based publications. Understandably, a standard list cannot be generated for a unique setting such as a Smart City. Hence, Smart Cities may use this list as a reference to fundamentally understand the tools and technologies for improving their disaster resilience, and next prioritise their options using a criterion to measure their potential.

The findings propose four-factor criteria to measure the potential of the tools/technologies available for improving disaster resilience. The four factors include impact on society, the adoption speed by Smart Cities, the maturity of the technology, and the capabilities offered to the community. This supports the fundamental argument that well-chosen tools and technologies help Smart Cities to coherently drive their strategy to achieve city goals such as disaster resilience. In fact, it is evident that an arbitrarily implemented selection of tools and technologies is a waste of resources and hinders the success of a Smart City project. Alternatively, the above four factors help to integrate the technology with society and find the right set and right mix of tools and technologies most suitable to any type of Smart City that intends to be disaster resilient. Although the above four factors were found in seeking the tools and technologies for disaster resilience in Smart Cities, the four factors are factual in assenting the potential of tools and technologies for any other segment of Smart Cities as well. Hence, the scope of this article was limited to only identifying key technologies and classifying them based on the aforementioned four factors. Further, the perspective from which the paper is written is limited to urban scholars. There have been no previous research findings for developing criteria to assess the potential of tools and technologies in Smart Cities as yet. Therefore, as a way forward, further research needs to be conducted along the four factors and validated in different Smart City contexts to derive further factors for different Smart City categories.