Disaster Management Systems | Encyclopedia MDPI

Disaster Management Systems: Comparison

Please note this is a comparison between Version 2 by Lindsay Dong and Version 1 by Imran Ashraf.

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. An increasing number of studies have elaborated on the importance and applications of remote sensing in disaster management. 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. Adequate systems and resources can ensure that the response to a disaster is coordinated and efficient and that the recovery time is minimal.

disaster management
natural disasters
floods
wildfire
earthquake
ecosystem

1. 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].

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, the 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.

2. Earthquake

2.1. Analysis of Earthquake Keywords

Figure 31 shows that there are five common ways to discuss earthquakes: vibration, seismology, tremor, seismic, and soil mechanism.

Figure 31.

Earthquake keywords.

2.2. Analysis of Detector Keywords

Figure 42 demonstrates earthquake detection-related terms used in existing works.

Figure 42.

Ratio of the earthquake detector terms to number of authors.

2.3. 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.

3. 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.

Figure 53. Types of earthquake detector.

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.

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.

This entails placing an earthquake detector inside a primary structural part of a building or other structure that shakes during a seismic event ^[18]. Patents et al. ^[19] 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.

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.

A vibration sensor and power shut-off device ^[19] 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.

According to Dutta et al. ^[20], 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.

2.4. 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.

4 provides a summary of the articles in terms of their findings on the environmental impacts of earthquakes.

Figure 64. Earthquake effects on the environment.

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.

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 ^[21]. 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 fiber-reinforced plastic (CFRP). To improve structural integrity, CFRP offers benefits such as ease of handling, corrosion resistance, and high strength-to-weight ratio [40].

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 ^[22]. Researchers have investigated novel techniques to strengthen and retrofit reinforced concrete structures, such as the use of carbon fiber-reinforced plastic (CFRP). To improve structural integrity, CFRP offers benefits such as ease of handling, corrosion resistance, and high strength-to-weight ratio ^[22].

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].

3. Floods

3.1. 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 decision-making. Additionally, data analytics can be used to detect vulnerable populations, improve evacuation routes, and distribute resources effectively during flood events.

3.2. 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.

3.3. 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.

3.4. 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 flood-prone 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.

3.5. 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 cooperation, 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.

3.6. 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.

4. Applications Based on Remote Sensing for Disasters

4.1. 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].

4.2. 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].

4.3. Floods

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 ^[24]. 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 ^[24][25].

4.2. 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 ^[26]. Tralli et al. ^[27] 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 ^[28]. Satellite remote sensing imagery was successfully employed for deployment, data collection, and dissemination during disaster management operations following the Haiti earthquake ^[29].

4.3. 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]^[30]. 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]^[31]. 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]^[32]. 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]^[33].

4.4. 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:

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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