A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions

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Introduction
Natural catastrophes such as earthquakes, tsunamis, floods, forest fires, plane crashes, and viruses are becoming more common, posing major challenges not just for the public but for government organizations in charge of disaster management and preparedness.Recent failures to respond to natural disasters, such as the H1N1 pandemic (i.e., the management of the swine flu) which arrived on Australian shores through the cruise ship industry in 2009 [1] and the earthquake in Haiti have sparked concern.In Victoria, Australia, bushfires have been a recurring challenge, often exacerbated by a lack of timely availability of skilled resources and a failure to harness the potential for skill reuse.Such shortcomings in disaster management can lead to catastrophic outcomes [2].Frequently, the potential for skill reuse is neglected with disastrous consequences.Therefore, utilizing the disaster management metamodel, in this work we suggest a method for integrating disaster management information to develop a flood and disaster support system that incorporates several disaster management (DM) operations tailored to a particular disaster.This strategy is inspired by method engineering, a knowledge management practice in software engineering.
DM manages disaster risks and effects.DM covers mitigation, readiness, response, and recovery [3].DM involves organizing, directing, and using counter-disaster resources [4].This domain's practitioners attempt to decrease or prevent natural disasters, assist disaster victims, and recover quickly.Operationalizing this domain involves many difficult activities.Risk evaluations, readiness, emergency responses, rescue, relief distribution, and reconstruction are all included.Data modeling and communication are difficult.Instead of attempting a comprehensive model, this work suggests a metamodel that can link various imperfect models that try to systematically convey DM knowledge.This approach allows us to set up a hypothesis of general notions that affect how we perceive reality [5].Reality ought to be influenced by models [6].They must be true or faithful representations to ensure that the model can be used to answer questions about the world or predictably change the world.A metamodel that explains what can be articulated in legitimate knowledge domain models is the result of metamodeling.The metamodel provides information about models.In this case, a model means the DM solution model, which shows how DM activities and their parts (such as people, resources, and plans) should be coordinated for a given disaster.Failures typically emerge from the accumulation of a complicated chain of events, and are frequently accompanied by changes in environmental factors [7].
A wildfire is an uncontrolled event that occurs in an area of combustible vegetation and is characterized based on the fuel consumed, such as a forest or grass fire, often known as a bush fire in different regions of the world.Such flora offer a carbon-rich fuel source which, when paired with seasonally dry conditions, can have severe effects on ecosystems and the human population in an area [8].Lightning strikes, volcanic activity, arson, and the unintended consequences of agricultural land removal can all contribute to the occurrence of wildfires [9], which have existed for a long time and can be caused by both natural and artificial factors.For instance, it was determined that lightning was mostly responsible for the summertime fires which plagued the southeastern portions of the Australian continent in 2019-2020 [10].About 21,000 hectares of agricultural land went up in flames due to additional fires in Australia during the same time period, and investigators have concluded that arson was likely to blame [11].This conclusion is based on how much these fires were talked about in the news and on the idea that the arsonists were able to keep their secret because there were other bigger fires in the area [12].
Droughts, heat waves, seasonal weather, and El Nino's warming phase can increase wildfire risk.Furthermore, it is predicted that the effects of climate change could result in fire seasons that start sooner, terminate later, and cause more extreme fire weather conditions [13].Fast-moving and difficult to control flames that result in widespread fire damage and health problems will undoubtedly be brought on by climate change [14].Particulate matter (PM), polycyclic aromatic hydrocarbons (PAH), ozone, carbon monoxide, nitrogen dioxide, and volatile organic compounds are among the air contaminants that are typically present in wildfire smoke, and can all be harmful to human health [15].Ophthalmic and psychological issues can arise, as can serious burns needing treatment in specialized burn centers, which frequently result in multiple organ failure as a complication of complex trauma.Respiratory and cardiovascular ailments are the primary health effects of air pollution, though they can cause ocular and psychiatric difficulties as well [16].In addition to fire, throughout the world earthquakes continue to be the main cause of death and damage due to disaster [17].In underdeveloped countries the death toll from earthquakes can reach shockingly large numbers; for instance, in Haiti 220,000 people lost their lives, and in Wenchuan 88,289 people died because of an earthquake.
According to historical records, the Sumatran fault and Sumatran subduction are the two fault zones that can impact Malaysia and Singapore [18].Sabah, which is in Malaysia, is currently under threat from a series of earthquakes.The 30 s long 6.0-magnitude quake that hit Ranau, Sabah, Malaysia on 5 June 2015 was the biggest to strike Malaysia since the 1976 Sabah earthquake.
Because of this tragedy, there was substantial property damage and loss of life.A simple earthquake detector has proven to be beneficial in notifying individuals when an occurrence is about to occur.Existing seismic instrumentation and communication technologies necessitate the development of an automated earthquake early warning system.This method can alert users ranging from a few seconds to a few tens of seconds before an earthquake generates severe tremors in the earth [19].New earthquake detectors have been used to find earthquakes and record their magnitude, with larger number indicating more severe earthquakes.
An increasing number of studies have elaborated on the importance and applications of remote sensing in disaster management [20][21][22][23].A major reason for the adoption of remote sensing is that it is one of the fastest means of acquiring data for pre-disaster and post-disaster studies.It is used to provide data for damage assessment in a timely manner and to assist in evaluation and rehabilitation plans.During the pre-disaster phase, remote sensing can be applied to identify and develop adequate systems and resources before the occurrence of a disaster [24].Adequate systems and resources can ensure that the response to a disaster is coordinated and efficient and that the recovery time is minimal [25].
Remotely sensed data such as MODIS, ASTER, Landsat, and Radarsat are used to produce maps of hazard and disaster risks.Digital terrain data derived from GTOPO30, SRTM DEM, and LiDAR are used for hydrological and flood modeling.For example, Li et al. [26] used GTOPO30 data to analyze the global impacts of potential inundation due to predicted sea level rise.Aleem and Aina [27] carried out a similar study on Yanbu Industrial City, Saudi Arabia using SRTM DEM data.Figure 1 shows the block diagram of the approach taken in the present research.
In this systematic review, we present a review of three types of disasters.First, we discuss earthquakes and previous relevant works, including the distribution of existing works concerning research areas, findings, and the impact of earthquakes on the environment.This is followed by wildfires, where key aspects, conclusions, validation approaches, and techniques used for wildfire detection are elaborated.Lastly, existing research on flood detection and management is discussed; this part covers the use of data analytics, machine learning, Geographical Information System (GIS), model-driven engineering, Internet of Things (IoT) networks, big data, and cloud computing for flood detection.In addition, we analyze the current challenges associated with fire, earthquake, and flood prediction and management as well as possible future research directions.
The rest of this survey is categorized into five sections.Our research methodology is explained in Section 2; earthquakes are covered in Section 3; Section 4 discusses works on wildfires; Section 5 elaborates flood-related works; applications based on remote sensing are elaborated in Section 6; and current challenges and future research directions are discussed in Section 7. Finally, Section 8 concludes the survey.

Research Methodology
To carry out the study process known as a systematic review for the topic of disaster management systems, we used the methodology shown in Figure 2 for data collection and analysis.The primary objective of this scholarly investigation was to conduct a comprehensive and in-depth examination and analysis of the body of literature concerning disaster management systems.To attain a robust and impartial review of the subject matter, a methodical approach was adopted.In this regard, an extensive and thorough literature search was undertaken using a diverse array of online libraries and academic databases to curate the findings.Priority was given to studies that significantly contributed to the advancement of knowledge in the field of disaster management systems.To identify relevant studies, a set of specific keywords encompassing "disaster management systems", "Remote Sensing", "approaches", "challenges", and "future directions", along with disaster types such as "floods", "wildfires", and "earthquakes", was employed in the search process.
Third, high-quality databases, including IEEE, Springer, MDPI, and Elsevier were used for data collection.These databases were chosen for their dependability and wide coverage of research articles on disaster management systems.
Fourth, we employed inclusion and exclusion criteria to select the most relevant and important studies.The criteria considered a variety of issues involving disaster management systems, such as various relevant techniques, obstacles, and potential future orientations.Our initial search results were examined and refined to focus on the most relevant research.
Fifth, an extensive examination of the current studies and publications on disaster management systems was part of our research methodology.Approaches, problems, risk assessments, emergency response planning, and the resiliency of infrastructure are among the topics discussed n the literature.Finding knowledge gaps, new trends, and research possibilities in the field were goals of this literature review.
Sixth, collected data and analysis from relevant data sources, such as research papers, reports, case studies, and successful implementations of disaster management systems, were assembled to support the conclusions of the literature review.This included successful implementations of disaster management systems.The gathered information can provide insights into the efficacy of various techniques, tactics, and criteria for disaster management.A comparative analysis was carried out to highlight the benefits and the drawbacks of the various techniques.
Seventh, the study process took legal and ethical considerations into account to ensure that all works were properly cited and all authors acknowledged.Another aspect of this consideration was to ensure that objectivity was maintained throughout the process of assessing and understanding the material.
To summarize, the methodology used for this study included a literature review, data collection and analysis, use of evaluation metrics, comparative analysis, and an assessment of any ethical concerns.A thorough grasp of the state of disaster management systems, including different approaches, difficulties, and potential future directions, was made possible by this comprehensive methodology.

Earthquake
The data presented in this analysis were obtained from an examination of various journals, and are categorized into four sections: keyword, output, type of earthquake detector, and seismic effect.The conclusions of this analysis are examined in depth in order to ensure the study's quality and to meet the review paper's purpose [19].

Analysis of Detector Keywords
Figure 4 demonstrates earthquake detection-related terms used in existing works.Table 2 shows earthquake terms used by the authors.Of the thirteen authors, several used varying terms instead of directly using the term "detector" in their studies, while the remaining authors used the term "detector".The term "sensor" was used by four authors: Mar et al. [43], Dutta et al. [44], Indiano et al. [45], and Huayong et al. [31].Additionally, two authors investigated "alarms" in their research: T.S.D et al. [46] and Baser et al. [47].

Findings on Different Earthquake Detectors
The findings on seismic detection are shown in Figure 5. Types of detection include S-wave, P-wave, detector, vibration, early warning, Arduino, alert, and level portions analysis.To reduce the damaging impacts of aftershocks, S-waves and P-waves are frequently used in the layout of quake detectors.The efficiency of an earthquake detector is significantly influenced by the selection of materials.The most effective way to gauge an earthquake's intensity and size while simultaneously avoiding false alarms is through vibration.Installing earthquake detectors improves human safety by providing early warning, allowing people to get ready beforehand and thereby minimizing fatalities.The primary wave, called the P-wave, causes particles to move in the same direction as the wave's propagation, carrying the energy of the wave.Conversely, the secondary wave, or S-wave, moves particles perpendicular to the wave's direction, either up and down or side-to-side.P-waves are essential in separating real earthquake signals from false earthquake signals produced by sound waves that push and pull the air.
Researchers have developed electronic devices to detect initial tremors resulting from surface fractures and to identify the primary and surface waves in elastic rock mediums.This invention pertains to earthquake detection and focuses on filtering and detecting earthquakes with specific intensities over a wide area, allowing for early and accurate detection while minimizing false alarms [32,36,42,43,45,46,48].
This entails placing an earthquake detector inside a primary structural part of a building or other structure that shakes during a seismic event [45].Patents et al. [49] reported that when vibrations occur, the detector generates a signal to trigger an alarm.An effective earthquake detector should be capable of detecting moderate earthquakes without generating false alarms.
In a study by Sagarino et al. [47], a vibration sensor was connected to an Arduino to measure, display, and analyze earthquake acceleration, proximity, displacement, and linear velocity.When the magnitude reaches a level of 5 or above, the sensor sends electric impulses to a prototype earthquake-proof container, which instantly closes when the magnitude reaches this level.The vibration sensor senses earthquakes and displays the results on a screen.The authors used ADXL335 accelerometer-based seismic sensors and an Arduino minimum system.The P-wave data from the ADXL335 sensor was successfully buffered, calibrated, sent, and shown on a website [32,36,42,43,45,46,48].
A vibration sensor and power shut-off device [49] that integrates a pendulum switch made for universal movement is another item described in the literature.A pendulum switch closes and activates a solenoid in response to vibrations in any direction, turning off electricity at a particular switch in an electrical power line.A low-voltage circuit that was functioning before the power outage will continue to operating.
According to Dutta et al. [44], due to a lack of solid diagnostic antecedents for various geo-tectonic settings that has hampered earthquake prediction studies, an earthquake early warning system designed to mitigate seismic hazards in a region must have at least three sensors from distinct locations transmitting P-wave data on the same scale in order to prevent the transmission of false seismic waves.Through this technique, the validity of seismic waves can be guaranteed and erroneous signals minimized.

Analysis of Earthquake Effects on the Environment
Earthquakes have significant impacts on the environment; the selected articles provide a comprehensive overview of these effects.The articles discuss various impacts, and include topics such as the development of Arduino detectors, advanced apps for early warning systems, innovative designs of earthquake-resistant structures, and other relevant issues.This indicates growing public concern regarding earthquakes and their consequences.Professionals in the field are actively generating novel and innovative ideas to address the challenges associated with earthquakes and improve public safety.This research paper primarily focuses on earthquake detectors, aiming to gather knowledge from scientific articles to develop new detectors or enhance existing ones.Figure 6 provides a summary of the articles in terms of their findings on the environmental impacts of earthquakes.When energy is released from the earth's crust during a seismic event, seismic waves are sent into space.These waves can vary in intensity, ranging from mild vibrations to significant earthquakes.Detecting and monitoring seismic events requires specialized equipment to accurately identify and analyze the seismic waves.One study implemented a compact earthquake alert system that utilizes light emitting diode (LED) displays to provide alert messages, helping people become aware of impending earthquakes and take necessary precautions to protect their lives [41].The portability and versatility of such a system makes it suitable for installation in various areas, enhancing earthquake detection capabilities.
Buildings are particularly susceptible to structural harm and collapse during earthquakes.The rumbling that occurs during earthquakes is brought on by seismic waves moving through rock.Most seismic occurrences take place near geologic faults when rock masses shift in relation to one another.As a result, buildings can suffer severe structural damage during earthquakes, highlighting the importance of earthquake-resistant design and construction practices [40].Researchers have investigated novel techniques to strengthen and retrofit reinforced concrete structures, such as the use of carbon fiberreinforced plastic (CFRP).To improve structural integrity, CFRP offers benefits such as ease of handling, corrosion resistance, and high strength-to-weight ratio [40].
In addition to their immediate effects on buildings and other infrastructure, earthquakes can have an adverse effect on the environment and public safety.Earthquakes can impact on air quality, ground and surface water quality, and other environmental factors.Effective preparedness strategies are essential to reducing the threats that earthquakes pose to the environment.It is crucial to remember that seismicity can occur when waste water from processes such as hydraulic fracturing is disposed of.Waste water injection as a way to dispose of fluid waste has been linked to artificial seismic activity.This takes place when the waste water injection, which is frequently disposed of in underground injection pits, causes seismic activity due to elevated pressure and movement of underground fluids [38].
Therefore, a comprehensive understanding of seismic events, their effects on the environment, and the development of effective detection and monitoring systems is essential.By studying scientific articles and research, valuable insights can be gained to produce earthquake detectors, innovative solutions, and improved preparedness measures.These efforts contribute to a greater awareness of the impacts of earthquakes and help mitigate risks to both the environment and the public.

Wildfires
Based on the bibliometric study, Table 3 shows the top five publications referenced for modeling wild fires in order to provide insight into the most popular works.The two authors with the highest number of citations for their computational fluid dynamics (CFD)based compositions were Linn in 2005 and Lopes in 2002.These two models have 118 and 100 citations, respectively.Both pieces serve as early models for detecting the growth of wildfires.

Ref. Model Key Aspects Conclusion
[50] CFD Investigation of grassland fire behavior aspects The size, shape, and ambient wind all affect ROS.
[51] GIS/CFD Putting in place a semi-empirical model to calculate the ROS.
Planning the control of fires using realistic simulations of wildfire progression.
[52] CFD A model's coefficients are optimized to replicate the impact of trees.
Successful comparison of various model tests to field measurements.
[53] Mathematics A description of the software architecture, numerical algorithms, and physical model.
Taking the level-set approach into consideration, the model might support real runs.
[54] GIS LANDFIRE's history and current applications are described.
LANDFIRE offers the tools necessary to create affordable fuel treatment options.
Table A1 in Appendix A provides a comprehensive overview of the evaluated research articles, concentrating on key aspects such as the authors, their specific work, validation/results, and employed techniques.The authors represent a wide variety of research works and professionals who have made contributions to the field of earthquake detection and related topics.Each article's conclusion summarizes the study's findings and outcomes while spotlighting the authors' specific contributions.The validation/results column presents the main results, outcomes, or validation methods utilized in each study, demonstrating the efficacy and dependability of the proposed methods.Finally, the techniques column describes the methodologies, techniques, or instruments used by the authors to conduct their research, providing insight into the scientific methods used to address fire-related challenges.

Floods
Floods are another common natural disaster that greatly influences the lives of humans along with the habitat, animal life, and atmosphere.A large range of tools and techniques have been deployed for flood detection in the existing literate; these are discussed under separate headings in the following subsections.

Data Analytics and Machine Learning for Floods
Machine learning and data analytics techniques can be incorporated into flood disaster management systems to improve flood forecasting, risk assessment, and response planning.These techniques can analyze large amounts of historical and real-time data, such as rainfall patterns, river levels, and meteorological conditions, to identify patterns, trends, and potential flood events.Machine learning algorithms can acquire knowledge from historical data to improve the accuracy of flood forecasting and facilitate proactive decisionmaking.Additionally, data analytics can be used to detect vulnerable populations, improve evacuation routes, and distribute resources effectively during flood events.

Geographical Information System and Spatial Data Management
By combining and analyzing spatial data related to flooding, the geographical information system (GIS) plays a significant role in flood disaster management systems.GIS facilitates the creation of accurate flood hazard maps by combining elevation models, hydrological data, and land use information, helping to identify flood-prone areas, infrastructure at risk, and high-risk zones.In addition, GIS facilitates spatial analysis and modeling in order to simulate flood scenarios, evaluate various flood management strategies, and support decision-making processes.GIS can help with real-time tracking and visualization of flood events, which makes it easier to respond quickly and use resources well.

Model-Driven Engineering
The model-driven approach or Model-Driven Engineering (MDE) is a systematic method that employs models to design, analyze, and simulate complex systems, including flood disaster management systems.Models that depict many components of the flood management procedure, such as flood forecasting, risk assessment, and emergency response planning, can be created using MDE.These models allow stakeholders to comprehend system behavior, evaluate various strategies, and maximize resource allocation.MDE facilitates the integration of various system components, promotes interoperability, and facilitates collaborative stakeholder decision-making.

Sensors Network and Internet of Things
Both sensor networks and IoT have the capability of real-time data collecting as well as monitoring for flood disaster management systems.By deploying sensors in floodprone areas, such as riverbanks or municipal drainage systems, continuous data can be acquired on flood levels, the extent of rainfall, and infrastructural states.IoT and sensor networks facilitate the early detection of flood events, aid in the evaluation of flood impact, and provide vital data for decision-making.The data from these networks can be combined with data from other systems such as GIS to track and manage floods more completely.

Big Data Analysis and Cloud Computing
This analysis method provides adaptable and effective approaches for handling and processing the substantial amounts of data linked to floods.Cloud-based platforms can store and process data from a variety of sources, including sensor networks, satellite imagery, and historical records.Data mining, pattern identification, and predictive modeling can yield important insights from these data.Real-time data processing, stakeholder co-operation, and the creation of data-driven decision support systems for flood catastrophe management are made possible by cloud computing and large-scale data analysis.By utilizing historical data, these technologies can assist in enhancing flood forecasts and planning for resiliency.
Data analytics and machine learning, GIS, MDE, sensor networks and IoT, and big data analysis and cloud computing are the major areas that the existing literature on flood prediction and management has focused upon.

Google Earth Engine
Google Earth Engine is a cloud-based platform developed by Google that provides a powerful and scalable environment for analyzing and processing geospatial data from satellite imagery and other Earth observation sources.It was launched in 2010, and is primarily used for conducting large-scale geospatial analysis, monitoring environmental changes, and supporting scientific research related to Earth sciences.
Google Earth Engine hosts an extensive archive of satellite imagery and geospatial datasets, including Landsat, Sentinel-2, MODIS, and others, dating back several decades.The platform allows users to access and analyze this vast collection of data using Google's computing infrastructure, which includes thousands of servers, enabling quick and efficient processing of large-scale datasets.Table 4 provides an overview of the advantages and disadvantages of each of these approaches.In this review, we found around 45 papers on flood prediction and management, with each paper using different techniques.Table A2 in Appendix A provides a comprehensive overview of the evaluated research articles, concentrating on key aspects such as the authors, their specific work, validation/results, and employed techniques.Table A2 presents a wide variety of researchers and professionals who made contributions to the field of flood disaster and related topics.Each article's conclusion summarizes the study's findings and outcomes while spotlighting the authors' specific contributions.The validation/results column presents the main results, outcomes, or validation methods utilized in each study, demonstrating the efficacy and dependability of the proposed methods.Finally, the techniques column describes the methodologies, techniques, or instruments used by the authors to conduct their research, providing insight into the scientific methods used to address flood-related challenges.

Applications Based on Remote Sensing for Disasters
The complexity of disaster management arises from the unpredictable and diverse nature of disasters, making it impossible to find a single comprehensive solution.Instead, a wide range of remote sensing platforms and sensors can and should be utilized for image acquisition [24].In the following section, we explore specific instances in which remote sensing has been applied in disaster management contexts.Furthermore, readers interested in delving deeper into this topic can find additional studies in the existing literature.

Wildfires
Wildfires, like other disasters, pose threats to life and property; moreover, they contribute to carbon emissions.Remote sensing data can prove valuable in fire detection, monitoring, modeling, and burnt area mapping.Satellite sensors with high temporal resolution, such as GOES (Geostationary Operational Environmental Satellite) and SEVIRI (approximately 30 min), have been utilized for fire monitoring [55].Sensors with thermal and infrared capabilities, such as MODIS and AVHRR, can be employed as well.Burnt area mapping is accomplished through a multi-temporal comparison of NDVI using visible and near-infrared sensors [55,56].In Europe, the European Forest Fire Information System (EFFIS) established by the Joint Research Centre and European Commission provides current and historical forest fire information using remote sensing data.Furthermore, remote sensing data have been employed to quantify forest fire contributions to carbon emissions [57].

Earthquakes
Earthquakes are natural disasters associated with earth movements, while landslides result from mass movements.Predicting earthquakes and volcanic eruptions remains challenging, limiting earthquake disaster management to preparedness and relief.Remote sensing has proven highly valuable across all phases of volcanic eruption disaster management, while its usefulness for earthquakes and landslides is somewhat limited [58].Tralli et al. [59] suggested that high-resolution digital elevation models (DEMs) such as InSAR and LIDAR combined with in situ data and imaging spectroscopy, e.g., ASTER, MODIS, and Hyperion, can aid in assessing and monitoring volcanic and landslide hazards.Sensors such as ASTER can be utilized to monitor earthquake-induced landslide dams for hazard mitigation in case of dam breach [60].Satellite remote sensing imagery was successfully employed for deployment, data collection, and dissemination during disaster management operations following the Haiti earthquake [61].

Floods
Flooding comprises various types, such as river floods, flash floods, coastal floods, and dam breaks, each exhibiting distinct characteristics with respect to occurrence time, magnitude frequency, duration, flow velocity, and areal extent.Satellite data have been effectively utilized throughout the multiple stages of flood disaster management [62].The GOES satellite's multi-channel and multi-sensor data sources are employed for meteorological evaluation, interpretation, validation, and the development of numerical weather prediction models.Additionally, they can aid in assessing hydrological and hydro-geological risks [63].Nonetheless, the use of optical sensors for flood mapping is constrained by the substantial cloud cover prevalent during flood events.To overcome this limitation, Synthetic Aperture Radar (SAR) and RADARSAT have demonstrated significant utility in flood mapping [64].It is essential, however, to integrate remote sensing data and GIS data during flood management, particularly in disaster relief operations.In summary, remote sensing data find application in flood management for hazard assessment map preparation, hydrological model generation, quantitative soil assessment, flood risk mapping, and early warning [65].

Interconnections Between Techniques
Our review of 29 research papers on how fires behave, how to predict them, and how to find them shows important insights and links in the field, as shown in Table A3 in Appendix A. These papers show how important historical data and fire maps are for determining how fires spread in different weather situations and on different types of land.These data can help to improve fire prediction models by considering variables such as weather, fuel moisture, and fuel load, and can be used with machine learning methods.The goal is to ensure that there are good tools for finding fires, predicting their spread, and determining the flammability of materials.High-resolution fuel information is important for capturing fire behavior accurately.The papers explain how fire behavior is related to different factors, testing methods, and statistical models for predicting fires on a small scale.This review provides important information that can help us to learn more about fires and how to control them, in turn leading to better safety measures for communities and ecosystems.The other publications on floods shown in Table A4 in Appendix A concentrate on employing cutting-edge technology and approaches such as machine learning, decision support systems, and IoT to enhance flood risk management.The goal is to develop new ways to prevent flood tragedies and make it easier to prepare for and respond to them.Integration of these technologies, along with data-driven models and teamwork between stakeholders, is a key part of reducing flood risk and making predictions accurate.More study and real-world usage are needed to fully use the potential of these approaches in real-world disaster situations in order to advance disaster management and risk reduction across different fields.

Impacts of Disaster Management System on Human Beings
People's social, cultural, and economic lives can be greatly affected by technical problems in emergency management systems; these effects can include: i.
Loss of Life and Injury: Technical problems in crisis management systems can slow down response and rescue efforts.This may cause delays in reaching affected areas, resulting in a larger number of casualties and injuries.ii.Psychological Distress: When emergency management systems do not work well, people may feel alone and overwhelmed.This can make them feel more stressed, anxious, and traumatized.iii.Disruption of Social Networks: When a disaster strikes, people often band together to help each other.Technical problems with crisis management systems can make it hard for people and communities to talk to each other and coordinate their relief efforts.iv.Loss of Culture: Sometimes, disasters can damage or destroy culturally important locations and artifacts.Technical problems could make it harder to maintain and protect these important parts of cultural identity.v.
Economic Loss: Technical flaws can stymie disaster response and recovery efforts, resulting in protracted downtime for businesses and key infrastructure.People, companies, and governments may lose money because of such disruptions.vi.Inequality and Vulnerability: Vulnerable individuals, such as the elderly, disabled, or disadvantaged communities, may experience additional difficulties obtaining resources during catastrophes.Technical problems can make these differences worse, placing groups even more at risk.vii.Migration and Displacement: If emergency management systems do not provide people with the right information or help, they may have to move or be moved in order to obtain help and resources.viii.Lack of Information: During disasters, timely and accurate information is critical.
Technical problems with communication systems can cause people to receive the wrong information, resulting in confusion and fear.ix.Interconnected Disasters: In complex disasters with multiple events, technical problems can make it hard to obtain a full picture of events, in turn making it difficult to coordinate reactions.x.
Loss of Trust in Institutions: Persistent technological failures in disaster management systems can erode public trust in government agencies and other institutions responsible for disaster response and management.xi.Long-Term Recovery Challenges: It might be difficult to plan for effective recovery and mitigation plans when technical issues that hamper data collection and analysis make it impossible to estimate the long-term effects of catastrophes.
To lessen these effects, it is important to invest in strong and effective disaster management systems, test and update them regularly, and make sure that communities are well-informed and involved in efforts to prepare for disasters.In addition, supporting a disaster management strategy that considers the different needs of all people and communities can help make people less vulnerable and strengthen their ability to deal with technical problems.

Challenges and Future Directions
Different disasters such as earthquakes, fire, and flooding have their own challenges and problems, in addition to the general challenges that are associated with natural disasters.

General Challenges
In this section, we first discuss general challenges in disaster management systems, then highlight challenges related to each category through short descriptions. i.
Effectively allocating limited resources such as personnel, equipment, and funding poses a challenge in disaster management, especially during simultaneous or resourcescarce events.ii.Ensuring effective communication and coordination among stakeholders is needed to overcome issues such as complex networks, language barriers, and coordination problems between agencies and organizations.iii.Gathering, analyzing, and disseminating timely and accurate information can be challenging during rapidly evolving disasters due to obstacles such as limited data, misinformation, and communication breakdowns.iv.Engaging local communities and promoting their resilience presents challenges due to cultural differences, distrust, limited awareness, and resource constraints.v.
It is necessary to adapt disaster management systems to cope with the changing characteristics and impacts of disasters, including climate change and urbanization.vi.Overcoming challenges in collaboration across disciplines such as emergency management, engineering, social sciences, and public health, which have differing terminologies, approaches, and priorities, can be difficult.vii.Ensuring sustainable and equitable recovery, addressing vulnerabilities, and integrating disaster risk reduction into development planning pose challenges beyond the immediate response phase.

Challenges Related to Floods
The following challenges are faced by flood management systems: i.
The need for constant improvement in domains related to floods (model-driven, machine learning, GIS, etc.) to enhance the speed and interpretability of flood-related models.ii.Lack of information on the computational time associated with flood modeling, hindering the evaluation of model applicability in disaster management.iii.Limited development of feature selection techniques needed to increase the efficiency of decision support in flood management.iv.Scarcity of studies comparing the computational efficiency of different simulation platforms for flood management.

Challenges Related to Earthquakes
In addition to general challenges, earthquake management systems have the following specific challenges: i.
Insufficient exploration of global models that can be applied to different regions and datasets in earthquake prediction along with limited assessment of their potential for generalization.
ii. Limited integration of earthquake protection concerns in multi-functional landscape management planning considering multiple ecosystem services.iii.Inadequate analysis of the minimum amount of data required for useful earthquake modeling, particularly during the active disaster stage.iv.Challenges in dealing with uncertainties in earthquake forecasting models and their impact on management decisions.

Wildfire Challenges
Challenges in the context of wildfire management include: i. Lack of extensive wildfire datasets for training models; most models are developed using smaller wildfire events, and may not adequately represent extreme wildfire contexts.ii.Need for advances in acquiring landscape dynamics data to quantify spatial patterns and changes in wildfire management.iii.Addressing issues of model overfitting in wildfire prediction and management.iv.Bridging the gaps between monitoring, learning, and decision-making in wildfire management.v.
Developing broader models that can integrate different stages of wildfire management effectively.

Limitations and Challenges in Using Remote Sensing for Disaster Management
Limitations and challenges when using remote sensing data in disaster management contexts include: i.
Finding the most applicable remote sensing system for a given type of disasters.ii.The need to evaluate the nature of the disaster and select appropriate sensors while considering spectral and temporal resolution as well as cloud coverage limitations; notably, the synergistic approach suggested by Joyce et al. [23] and Leblon et al. [55], involves combining visible sensors with microwave sensors to overcome cloud coverage effects.iii.Available frameworks for using remote sensing in disaster management are limited [23]; moreover, there is a need to develop new frameworks or templates for applications in remote sensing for disaster management without "reinventing the wheel".iv.Challenges around timely provision of data mean that developing countries in particularly face limited access to certain data (e.g., high-resolution images) and may lack the technical expertise to handle it.v.
Lack of research funding for application of remote sensing data in hazard management contexts limits the effective use of satellite data [21].
Based on the above, it is important that new research studies investigate the application of new algorithms and frameworks for remote sensing in disaster management.

Future Directions
Future research should prioritize the development of new and improved approaches to detection, warning, prevention, mitigation, and response across all disaster types by considering the above issues with the overarching goal of advancing more effective and integrated disaster management systems that prioritize the protection of lives and property.By uniting these future directions, we anticipate a safer and more resilient world better equipped to face the challenges posed by natural disasters.In the realm of disaster management, remote sensing holds in particular holds immense potential to revolutionize response efforts in the event of earthquakes, wildfires, and floods.Advances in integrated remote sensing systems offer versatile solutions to address a range of disaster types, addressing challenges such as cloud coverage and temporal resolution limitations.Standardized frameworks and templates can streamline numerous processes, promoting efficient and effective responses in different disaster scenarios.Timely data provision remains a priority, particularly in developing countries where access to high-resolution images and technical expertise may be limited.By investing in capacity building, research funding, and international collaboration, the future of remote sensing in disaster management promises enhanced preparedness and resilience, paving the way for more effective earthquake detection and warning methods, improved coordination between agencies, better fire suppression techniques in wildfire management, and a comprehensive approach encompassing forecasting, early warning systems, and infrastructure floodproofing in flood management.

Conclusions
This systematic review concludes with a comprehensive evaluation of current disaster management systems, with an emphasis on earthquakes, wildfires, and flooding.We have reported promising strategies and identified challenges that must be addressed in order to improve preparedness, response, and mitigation.For earthquake management, seismic monitoring and early warning systems show promise, although data collection and communication strategies must be improved.Prescribed burning and fireproofing are two strategies that are becoming more popular in wildfire control, although coordination and fire suppression methods remain an issue.Likewise, flood management necessitates a comprehensive approach involving flood forecasting, early warning systems, and floodproofing of infrastructure, necessitating new developments in data collection, analysis, and evacuation strategies.This review highlights the importance of remote sensing technology, particularly in those phases of disaster management that involve preparation, warning, response, and monitoring.Combining remote sensing and GIS techniques can greatly improve efficiency; however, there are obstacles and limitations to using satellite data for disaster management that necessitate additional research.Future research should concentrate on developing innovative and enhanced detection, warning, prevention, mitigation, and response strategies.Using remote sensing data in conjunction with other surveying and monitoring techniques can improve disaster management with the objective of developing more efficient systems that safeguard lives and property during disasters.The findings of this review can serve as a basis for future research and innovation in disaster management, as natural disasters continue to pose significant global challenges.Adopting these insights will result in a safer and more resilient future for everyone, enabling communities to be better prepared for natural disasters.Constantly striving for development can help to mitigate the effects of these catastrophic events and place the well-being of affected populations at the forefront.
Funding: This research was supported by the European University of the Atlantic.

Conflicts of Interest:
The authors declare no conflict of interest.
Appendix A Table A1.GIS, mathematical, CFG and ML-based techniques for wildfire management.

Ref. Key Aspects Conclusions with Specific Work Validation/Results
Technique [66] Based on past data, create a monthly fire spread probability model.
Historical data and the likelihood of fire spread are significantly correlated.Historical data GIS [67] Analysis of wildfire exposure and risk transfer in Sardinia using wildfire simulation modeling The major conclusions can be applied to further assess the likelihood of transmission or anticipated wildfire behavior.

Historical data GIS
Table A1.Cont.

Ref. Key Aspects Conclusions with Specific Work Validation/Results
Technique [68] Introduction of Fire Map, a web platform for geospatial data services and fire prediction, including its architecture and main components.
Fire map has proven valuable in the firefighting community, providing automatic fire perimeter detection and reliable future forecasts.
-GIS [69] Presentation of a methodology that combines automated wildfire monitoring with accurate fire spread forecasting.
The backward time method is a simple and effective approach for solving fire prediction.
Experiments GIS [70] Proposal of various algorithms for fire behavior analysis using the final perimeter as input.
The developed tool is efficient and fully functional.
-GIS [71] Discussion on integrating two models into a GIS-based interface The tool developed is efficient and fully operational.

Historical data GIS [72]
Introduction of a model that evaluates the impact of five landscape factors on fire spread performance.
Categorizing fires based on synoptic weather conditions can enhance fire modeling in landscape fire models.

Historical data GIS [73]
Exploration of multi-fidelity approaches to fire spread prediction.
Fuel moisture, fuel load, and wind speed are the main uncertainties affecting fire spread rates.
Experiments CFD [74] Investigation of how fuel density and heterogeneity affect fire behavior in relation to wind characteristics.

Increasing canopy fuel structure detail and implementing turbulent boundary conditions have minimal impact
Experiments CFD [75] How fuel density and heterogeneity affect fire behavior in relation to wind characteristics.
Incorporating high-resolution fuel fidelity and heterogeneity information is crucial to capture effective wind conditions.
-CFD [76] Considers the effect of vegetation characteristics on the flame tilt angle and the radiative heat transfer.
High-resolution fuel fidelity and heterogeneity information are vital for accurately capturing wind conditions.
Simulations CFD [77] Investigate fire regime transition and its associated heat transfer mechanisms.
The model predicts both free and non-free fires, introducing new models for tilt angle and radiative heat power reaching vegetation.
Simulations CFD [78] a fresh simulation tool for quick resolution, atmosphere fire reaction.
The ability to record fundamental patterns in fire behavior, the relationship between fire spread and fire size, and the use of canopy fuels.

Compared With other models CFD [79]
The multiphase model is created and added to PHOENICS.
The anticipated ROS and experimental values measured at varied wind speeds were in good agreement.
Experiments CFD [80] Outlines a method for creating a burned area probabilistic forecast The calibrated ensembles improve accuracy overall.Simulations Mathematical [81] Creation of a computer model to forecast soil organic matter loss The amount of water in the soil regulated the amount of heat used during vaporization and stopped soil deterioration.
Simulations Mathematical [82] Creating a fire spread model using a heterogeneous cellular automata model.
The model can anticipate the spread of a fire with a respectable level of accuracy and efficiency.
Simulations Mathematical [83] Prediction of the spread of a surface fire with an emphasis on uncertainty.
Based on probabilistic fire simulations, maps of the potential for fire can be created.

Ref. Key Aspects Conclusions with Specific Work Validation/Results
Technique [84] In an upslope fire spread model, parametric uncertainty analysis is developed.
The projected values of ROS under lower slopes are significantly impacted by the values of ignition and flame temperatures.
Simulations Mathematical [85] Creation of two empirical ROS functions in a windless environment.
Both models demonstrate that independent variables serve as suitable ROS descriptors.
Simulations Mathematical [86] Creation of an empirical model for ROS assisted by wind.
A laboratory examination of the Rothaermel model revealed improved predictions.
Simulations Mathematical [87] Support      Table A3.Interconnections and synergies among techniques for wildfires.

Figure 1 .
Figure 1.Block diagram showing the workflow followed in this review.

Figure 2 .
Figure 2. Methodology adopted for the literature review.

Figure 4 .
Figure 4. Ratio of the earthquake detector terms to number of authors.

Figure 6 .
Figure 6.Earthquake effects on the environment.

Table 1 .
Usage of earthquake-related terms listed by author.

Table 2 .
Earthquake detection-related terms listed by author.

Table 3 .
Most cited papers about wildfires.

Table 4 .
Advantages and disadvantages of techniques/domains used for flood disaster management.

Table A2 .
Flood management techniques related to machine learning, GIS, MDE, IoT and GEE.