Natural and human-made disasters, including hurricanes, floods, bushfires, avalanches, droughts, epidemics or pandemics, and terrorist attacks, have drastic effects on human beings, societies, economies, and the environment. According to the International Federation of Red Cross and Red Crescent Societies (IFRC) World Disaster Report 2020, disasters caused by climate change have surged by 35 percent over the last decade. A total of 400,000 people have died in these calamities, and 1.7 billion people have been affected [1
]. According to the United Nations University—The Institute for Environment and Human Security (UNU-EHS) Interconnected Disasters Report 2020/2021, the world has witnessed several record-breaking disasters during the year 2020, including the COVID-19 pandemic, Texas cold wave, Amazon wildfire, Vietnam heavy storms, and Amphan cyclone on India–Bangladesh border [2
]. These global disasters have affected or killed hundreds of people and caused billions of US dollars in damage. This scenario has fostered an increasing interest in the development of systems designed to support Search and Rescue (SAR) processes.
Whenever a disaster strikes, SAR services responsible for handling emergencies are required to respond. SAR services refers to the process planned by authorities to save people from death or injuries in the case of serious disasters that are not handled by specifically established agencies or under specific measures [3
]. The SAR process establishes coordination between representatives from different organizations, such as the police, fire department, medical authorities, port authorities, armed forces, communication companies, air traffic services, civil defence, and voluntary organizations [4
]. Collaboration across organizations makes SAR processes extremely complex, and often includes a set of interdependent assignments with many degrees of detail [5
Typically, the SAR process is categorized into the following four phases, as shown in Figure 1
: mitigation, preparedness, response, and recovery. Disaster mitigation is a continual process designed to decrease or eliminate risk. Risk identification, analysis, appraisal, and risk mitigation through spatial planning, technical measures, public awareness, and education, are all part of mitigation [6
]. Preparedness is the process of deciding how to react to a disaster. Emergency planning and training, as well as the installation and operation of monitoring, forecasting, and early warning systems, are all included in this phase. In the case of a disaster, emergency response includes SAR activities and measures to meet the affected population’s basic humanitarian requirements. Finally, the process of repairing living conditions in disaster-stricken areas is known as emergency recovery. This entails prompt damage assessment, rehabilitation, and reconstruction.
To handle this level of complexity, it is necessary to utilize efficient and effective decision support. DSSs, data management solutions, and AI have been used extensively to help reduce the impacts of disasters. These technologies have received considerable attention in recent years and are being adopted by many different sectors, including business, healthcare, banking, telecommunications, government, and SAR, to gain a competitive edge [8
In the context of SAR, these technologies can be used to optimize time and cost by providing valuable insights and support to disaster management experts. This can help them to make more informed decisions and respond more quickly and effectively to emergencies. Data management solutions such as Information Systems (ISs) and Geographical Information Systems (GISs) can be used to collect, store, and analyze large amounts of data from various sources, such as sensor networks, social media, and satellite imagery. This information can be used to gain a better understanding of the situation and to identify patterns and trends that can help inform decision-making. AI, on the other hand, can be used to analyze data and make predictions about potential hazards or risks. For example, machine learning algorithms can be used to analyze satellite imagery to identify potential flood or wildfire hazards. Similarly, Natural Language Processing (NLP) can be used to analyze social media data to identify areas where people are in need of assistance.
The development of suitable DSSs requires the utilization and integration of several state-of-the-art technologies, such as information and communication technology (ICT) and telecommunications, to support SAR operations. Government authorities, researchers, and practitioners involved in SAR processes have been working to enhance these concepts by considering new ideas from research fields such as computer science, information technology, cybernetics, environmental sciences, and decision sciences. The goal is to improve the data collection, management, processing, and visualization phases of SAR processes for timely and precise decision-making.
Motivated by the significance of these concepts in the field of SAR, in this review we aim to systematize existing knowledge about the use of DSSs, data management solutions, and AI in SAR processes.
We formulated our overarching research question as follows: How do SAR processes use DSSs, data management solutions, and AI?
To address this question, we performed a bibliometric mapping and systematic literature review. More specifically, we investigated the literature to identify research patterns, as described in detail in Section 4.3
. The objective of our study was to identify existing solutions used for SAR operations and to provide practitioners with insight into knowledge transfer possibilities within SAR application areas.
We used Web of Science (WoS) for the bibliometric mapping and systematic literature review. Bibliometric mapping explores the data sample retrieved from a data source with the goal of characterizing the evolutionary dynamics of the research area by displaying evidence of the field’s emerging areas [9
]. In turn, a systematic literature review analyzes data while summarizing the existing evidence concerning an overarching research question. The results of this study can be beneficial for practitioners and researchers in the field of SAR by providing them with a comprehensive overview of the current state of the art in the use of DSSs, data management solutions, and AI in SAR operations. In addition, it can help to identify knowledge transfer possibilities and guide future research in this field.
The remainder of this paper is organized as follows. Section 2
discusses related works presented by various authors in this domain over the years. Section 3
defines and describes the concepts used in this study, specifically, the SAR process, DSSs, data management solutions, and AI technologies. The research methodology is presented in Section 4
. Moreover, protocol development, inclusion and exclusion criteria, dataset preparation, research questions, and bibliometric mapping are explained in detail. In Section 5
, we synthesize the bibliometric analysis and systematic review. Section 6
addresses the research questions using the synthesis provided in the previous section, and discusses with the potential future work. Finally, the study’s conclusions and limitations are presented in Section 7
2. Related Work
To the best of our knowledge, papers by [7
] have previously reviewed the literature related to SAR processes using various methodologies.
Hair Zaki et al. [7
] investigated existing flood disaster management systems by analyzing the incorporation of the system based on sentiment analysis and system-oriented architecture. In addition, they performed a comparative analysis of studies related to flood disastermanagement frameworks. Kaur et al. [10
] performed a scientometrics analysis of ICT use for disaster management. In their study, the authors investigated research activities from 2009 to 2019. For the scientometrics analysis, the authors utilized the Scopus database to examine the annual increment in publications, contributions to several domains, and the cooperation of different countries and authors. According to Kaur et al. [10
], the analysis showed that natural disasters such as floods and earthquakes were always at the forefront, with the maximum number of research articles, and that the USA, Japan, China, and India had the most remarkable collaboration with other countries in creating systems with the help of ICT. Nunavath and Goodwin [11
] presented a systematic literature review of the applications of AI, machine learning (ML), and deep learning (DL) in disaster management from 2009 to 2019. Their work relied on the Scopus database to identify the relevant articles. In their review, the authors categorized disasters as natural or human-made disasters in order to analyze the types of techniques used for prediction and classification in the mentioned categories. According to the authors [11
], the most common algorithms used for natural disasters are support vector machine (SVM), naïve Bayes (NB), convolutional neural networks (CNNs), Natural Language Processing (NLP), artificial neural networks (ANNs), reinforcement learning (RL), random forest (RF), decision tree (DT), logistic regression (LR), latent Dirichlet allocation (LDA), and k-nearest neighbor (KNN). In addition, they pointed out that the techniques most used for human-made disasters are RF, DT, CNN, NLP, KNN, genetic algorithms (GA), and multi-layered feed-forward networks (MLFFN). Ray et al. [12
] presented state-of-the-art technologies in Internet of Things (IoT) for disaster management. Their survey focused on the approaches used to provide early warnings or awareness, notifications, and support for data analytics related to disasters. The authors discussed IoT-supported protocols for wireless sensor networks and the deployment of these protocols on different operating systems. In addition, they provided research on IoT-enabled market-ready products for disaster management systems. These products use various sensors and communications protocols to provide early warnings about future natural disasters, such as earthquakes, tsunamis, and landslides.
In another article, Shah et al. [13
] conducted a thematic taxonomy of the importance of big data analytics (BDA) and IoT for disaster management. The designed taxonomy categorizes relevant concepts and essential parameters for BDA and IoT-based disaster management. The authors presented a conceptual reference model for BDA and IoT-based disaster management as a roadmap for future realistic applications. Another article by Minas et al. [14
] presented a survey with the goal of improving the theoretical foundation of modeling emergency response and enhancing the research in that domain. The authors performed a bibliometric analysis by adopting unsupervised learning and citation network analysis methodologies. Through a bibliometric approach, they classified the literature related to emergency response operations management into different clusters and indicated the types of modeling used for emergency response, including analytic models, decision analysis, stochastic models, and queuing models. Their findings revealed relationships between the diversity of emergency response models. Prasanna [15
] discussed the emergency response to fire emergencies. In their study, they carried out a survey on the outcomes of two previous preliminary studies on information and human–computer interaction based on discussions between end users, system architects, and designers. They evaluated the performance of Information Systems (ISs) architectures developed in previous studies using scenario-based action research. Based on their survey, the authors proposed an ISs architecture with particular key elements that were missing in previous studies; the overall objective of their study was to provide firefighters with better situational awareness through the use of ISs architectures. Shahrah and Al-Mashari [16
] conducted a preceding study on emergency response systems and the related challenges. They classified the research directions in the investigated domain into design principles and frameworks, standardization, agent-based simulations, web technologies, business process management, IoT, case-based reasoning, and expert systems. For each research direction, they described the existing research until 2017 and the shortcomings in each domain. Alotaibi et al. [17
] performed a review study of coordination in emergency response using agent-based simulation. The authors categorized the study field into three different parts in order to analyze the role of agent-based simulations. They concluded that the work carried out in connection with the coordination of multiple organizations in emergency response was limited in terms of agent-based simulation.
In addition to these reviewed articles, Cumbane et al. [18
] addressed the potential of big data sources and advances in data analytic techniques to extract geospatial information critical for rapid and effective disaster response. They compared processing frameworks and established a link between big data and processing frameworks for critical tasks in the disaster management response phase. Bomi et al. [19
] investigated ways to improve the efficiency of disaster management in South Korea through the use of a spatial database and image information combined with information regarding nuclear power plants. Jessin et al. [20
] analyzed the use of UAVs as a tool for data acquisition in coastal monitoring on island territories, highlighting the available platforms, sensors, software, and validation methods. They focused on operationalizing the concept of resilience as a risk management technique with the goal of linking analyzed data to a spatial decision support system, and used the French Polynesian islands as a case study.
Moreover, Gil et al. [21
] presented a bibliometric analysis and systematic literature review of shipboard DSSs for accident prevention. The authors selected studies from the WoS database to increase understanding of the structure and contents of the academic domain. They reviewed the top articles based on a standardized technology readiness level (TRL) of systems provided by NASA, showing how previous researchers have categorized DSSs in the context of preventing accidents at sea, for example, in collision-avoidance, ship maneuvering, and ice navigation.
Although these reviews present relevant results for researchers in the investigated domain, we observed the following limitations. First, none of the existing reviews have focused specifically on the “use of DSSs, data management, and AI for SAR processes”. The studies in [14
] focused on “emergency response systems”, while others such as [7
] concentrated on “disaster management”. The main difference between disaster management systems and an emergency response systems is that the former target large-scale disasters, whereas emergency response systems are mostly focused on small-scale emergencies. In our review, we consider both terms, namely, disaster management and emergency response systems, with respect to SAR operations, thereby broadening our perspective. Furthermore, we use terminology for both natural and human-made disasters. Existing reviews have characterized “the use of advanced technologies for SAR operations”, which takes into account fewer dimensions. In our review, eight dimensions are considered, including, among others, AI, data management solutions, DSSs, disaster management, emergency response, SAR operations, and type of disaster. Another difference is that each previous study compared articles using different reviewing methods, and only Nunavath and Goodwin [11
] carried out a systematic literature review using fewer dimensions and fewer tables and charts. Our review provides a bibliometric analysis of the investigated domain along with a systematic literature review. We believe that adopting these methods in our comparison of existing research allows us to identify the research patterns and trends in the area along with any existing gaps. In addition, we presume that there are opportunities for knowledge transfer among the different application areas involving SAR processes.
6. Discussion and Future Work
This section provides answers to our overarching research question, i.e., how SAR processes use DSSs, data management solutions, and AI. Our discussion is based on the synthesis provided in Section 5
, an includes a list of potential future work areas. The goal of the discussion is to identify and analyze the use of decision support in SAR. This includes identifying any gaps in the literature or areas where further research is needed. These results provide insight into the current state of the art in the use of these technologies in SAR operations as well as the potential future work that can be done to improve their effectiveness in decision-making and emergency response.
In our study, we identified the existing studies in this domain and responded to eight questions (Q1–Q8) by comparing the existing studies.
Q1 aimed to understand the effectiveness of using DSSs, data management solutions, and AI in SAR processes. Q2 and Q3 presented the most common disaster events and proposed solutions based on the selected articles. We observed that most of the selected papers focused on both natural and human-caused disasters. We looked into the use of DSSs, data management solutions, and AI in SAR processes, finding that the reviewed articles tended to focus more on planning than on carrying out plans. Q4 explored the scope of the studies in terms of the specified spatial dimensions. Our observations show that research predominantly involved disasters occurring in the USA or China.
Most of the presented solutions in the investigated articles aimed at SAR operations on land (fire emergencies, mountain SAR, urban SAR, and small river SAR). Comparing the existing solutions, we observed that there are few studies focusing on DSSs for SAR at sea, potentially motivating further studies in this specific application area. Regarding the latter, we observed that few articles focusing on SAR at sea used AI technologies for data analysis and presentation to handle a disaster. Moreover, data-centric domains, such as novel database architecture and data analysis and presentation, constituted a significant portion of the investigated articles. The aforementioned categories are focused on overall disaster management systems, most of which involve the use of UAVs. These observations revolve around Q5, which explores the data management solutions provided in the studies.
Q6 emphasized the type of data managed in the reviewed articles. Based on our findings, the reviewed articles mainly focused on geographical data, with very few focusing on historical data. Q7 addressed which AI technologies were utilized. We observed that a very small percentage of articles integrated AI technologies and data management solutions for decision-making in SAR. In addition, we noted that AI technologies were used with data analysis and presentation (ISs) and to provide solutions for the emergency response planning phase. Most ML techniques, on the other hand, were used in semi-structured databases that provide situational awareness in an emergency. DL techniques were used in semi-structured databases and programming, data structures, and algorithms used to train UAVs models for SAR operations on land.
Additionally, other technologies such as NLP, serious games, Petri nets, and multi-objective optimizations were found to be utilized in different data-centric domain categories to address SAR processes. For instance, Ni et al. [59
] used an NLP technique in developing an emergency response repository to build textual emergency response plans. Furthermore, Petri nets and workflow nets were presented in the context of cross-organizational emergency responsemodeling for better communication and resource distribution [62
]. Finally, Q
8 identified potential end users for the solutions provided in the investigated articles. We noticed that SAR experts were mainly the targeted users in the studied articles.
We found solutions presented in the literature for SAR processes on land that can be used in SAR DSSs at sea as well; for instance, Petri nets and workflow nets have been used to model DSSs for SAR processes on land [62
]. Furthermore, a system-of-systems approach was used by Fan and Mostafavi [39
] to develop a system able to identify and analyze the heterogeneity and complexity of SAR processes and develop plans accordingly. Big data systems such as cloud computing and fog computing have been used in SAR operations to aid disaster management [42
]. Potentially, all the above-mentioned techniques are applicable within SAR operations at sea. The possibility of utilizing these techniques most likely depends on the problem description, that is, which phase of the SAR process is the focus.
6.2. Future Work
The research patterns observed in the literature are discussed in Section 6.1
. Many researchers have provided enhancements and changes; in brief, all research goals require distinct techniques to accomplish the desired outcomes. However, the solution to each problem cannot be restricted to a single technique. We believe that there is a possibility of transferring knowledge among application areas of the SAR processes as the solution design process continues to grow.
For instance, as mentioned in Section 6.1
, there are only a very small number of articles focusing on decision-making for SAR at sea. In the future, solutions provided for use on land SAR could be used for SAR processes at sea as well. Moreover, techniques that have been used only in a specific manner to date could be adapted for various other problems.
In the context of SAR, DSSs, data management solutions, and AI are all used to optimize time and cost by providing valuable insights and support to disaster management teams. This can help them to make more informed decisions and respond more quickly and effectively to emergencies. The main objective of our study was to identify and compare the existing systems that make use of DSSs, data management solutions, and AI in the field of SAR processes. To achieve this, we used two different research methods, namely, bibliometric mapping and a systematic literature review. Bibliometric mapping was used to generate a data sample, while the systematic literature review was used to answer the overarching research question.
The scope of the study was analyzed in relation to the research objectives and overarching research question. The types of SAR considered in the study were identified, and based on this the research contribution of different studies to SAR processes was described. The use of DSSs, data management solutions, and AI technologies in SAR processes was determined, and the potential end users of the presented solutions were identified.
The findings of this review have include the identification of research gaps in the investigated domains, including a lack of articles focusing on SAR operations at sea. In addition, we have discussed possibilities for knowledge transfer between application areas, recognizing that by implementing advanced DSSs, data management solutions, and AI technologies it is possible for SAR organizations to make better decisions about where they need to invest more effort and improve communications in order to utilize their resources to their fullest potential.
The present review has a number of limitations common to bibliometric mapping and systematic reviews [21
], such as the year range, which only includes articles from 1 January 2017, to 10 November 2021, and the use of only WoS (Appendix A
) as the platform for identifying articles based on a predefined search string.
In conclusion, our study highlights that significant efforts have been made to improve SAR processes using DSSs, data management solutions, and AI technologies. However, there is room for improvement; for example, decision-making solutions for SAR operations can be expanded, and knowledge can be further transferred among the application areas of SAR processes.