PREVENT Introduction

Introduction to the Module and Natural Disasters

PREVENT Project - Chapter 1

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Chapter description:

This chapter provides an overview of the module and introduces the concept of natural disasters. It highlights their causes, types, and impacts on societies and ecosystems. Learners will understand the urgency of addressing climate-induced disasters and the critical role of innovative technologies in mitigating their effects.

Index

Definition of Nat. Disasters

Earthquakes

Wildfires

Floods

Epidemics

Extreme Weather

Objectives

Through this module, learners will gain a comprehensive understanding of natural disasters, their causes, impacts, and the role of advanced technologies in disaster prevention and mitigation. By exploring real-world case studies, scientific research, and interactive simulations, students will develop the ability to classify different types of natural disasters, analyze their social, economic, and environmental consequences, and evaluate the impact of climate change on disaster frequency and severity. Furthermore, learners will acquire critical thinking skills to assess disaster risk management strategies, understand how deep technologies such as AI, IoT, and satellite imaging contribute to early warning systems, and explore innovative disaster resilience frameworks.

By the end of the module, students will be able to apply knowledge in risk assessment, disaster response planning, and climate adaptation strategies, equipping them with essential skills for careers in environmental science, emergency management, and sustainable development.

'True learning begins when knowledge inspires action, and understanding drives change. Equip yourself to turn challenges into opportunities.'

'Harnessing the power of new technologies, we can predict, prepare for, and mitigate the impact of natural disasters—turning technology into a lifeline for our future.

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Understanding Natural Disasters

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Definition of Natural Disasters

A natural disaster is a sudden and catastrophic event resulting from natural processes of the Earth that lead to significant disruptions in human lives, ecosystems, and infrastructure. These events often occur unexpectedly, leaving communities vulnerable due to their destructive forces and long-term consequences. Natural disasters can be influenced by geographical, meteorological, or biological factors and are further exacerbated by human activities, such as deforestation and climate change. Key Characteristics of Natural Disasters:

• Unpredictability: Many natural disasters strike without prior warning.
• Widespread Impact: They affect vast areas, causing damage to life, property, and ecosystems.
• Long-term Effects: Impacts include economic loss, displacement, and environmental degradation.

Understanding Natural Disasters

Natural disasters are processes that can trigger natural disasters and could be classified into five categories: Geophysical or geological hazards that involve movement in the solid earth, such as earthquakes and volcanic activity; Hydrological hazards that pertain to water movement, including floods, landslides, and wave action; Meteorological hazards that encompass storms, extreme temperatures, and fog; Climatological hazards, increasingly linked to climate change, that include droughts and wildfires; and Biological hazards, that arise from exposure to living organisms or their toxic substances, with the COVID-19 virus being a notable exampleAddressing both the root causes of natural disasters and the socio-economic factors that exacerbate their effects is crucial for mitigating the overall impact of natural disasters. In this sense, technologies that mitigate, manage or prevent their occurrence play a crucial role in understanding and minimizing the consequences of natural hazards.

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Earthquakes

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Introduction to Earthquakes

An earthquake occurs when the strain energy in the Earth’s crust is suddenly released, causing in that way waves of shaking. Earthquakes are classified among the deadliest natural hazards which could cause terrible loss of human lives and economic cost. According to the National Earthquake Information Center, 20000 earthquakes are recorded on average every year, from which around 100 earthquakes could cause serious damage and 16 are considered major earthquakes (in the magnitude 7 range and above on Richter scale).

Numbers

80%

Extreme weather predictions

Except for the main earthquake, aftershocks or swarms of earthquakes may happen. The foremost happens from the sudden change in stress within and between rocks. Aftershocks are lower-magnitude earthquakes that follow the main shocks of a larger earthquake. Beyond the main earthquake, aftershocks and seismic swarms can occur due to the redistribution of stress along fault lines. AI-powered seismic monitoring systems now analyze vast datasets to detect subtle patterns, enhancing early warning capabilities and disaster preparedness.

+190

Countries The Global Seismic AI Network

90%

70%

AI-driven seismic sensors can predict earthquake aftershocks with over 90% accuracy

satellite data can predict flood-prone areas

How AI is Revolutionizing Earthquake Prediction and Response

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1,200M

90%

People at Risk in Seismic Zones

Real-Time AI Data Processing

AI-Powered Earthquake Prediction

More than 1.2 billion people live in earthquake-prone areas worldwide. AI-driven early warning systems can help reduce casualties and economic losses.

AI models process seismic data twice as fast as traditional methods, enabling real-time alerts and reducing response times for rescue teams.

AI-driven seismic sensors can predict earthquake aftershocks with over 90% accuracy, improving disaster response and preparedness.

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Mechanics and Dynamics of Earthquakes

The Earth's crust is comprised of seven large tectonic plates and several smaller ones. Beneath the Earth’s crust, there is a liquid mantle that exhibits characteristics similar to those of liquids. Due to convection currents in the mantle, resulting from heat transfer from Earth’s core, tectonic plates move several inches each year. The friction from colliding tectonic at their edges causes massive pressure which may exceed the frictional forces holding the plates together, resulting in a sudden stress release causing an earthquake.

Specifically, an earthquake occurs when the surface of Earth shakes in the lithosphere and in this way it creates seismic waves. The shaking happens from the sudden release of energy and the sudden movement of tectonic plates.

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Technologies used to deal with Earthquakes

There are multiple techniques used to predict and detect earthquakes. However, in cases where the earthquake cannot be detected at all or at an early stage, there are some techniques that can be used to mitigate the implications of earthquakes.

Technologies for Detection

Earthquake Early Warning (EEW)

Global Navigation Satellite System (GNNS)

Observation systems

Observation systems

Infrasound Technology

Geochemical Sensors

Seismometers

Machine Learning (ML) Techniques for prediction:

Accelerometers

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Technologies used to mitigate the implications of earthquakes

In the aftermath of an earthquake, assessing the affected areas becomes a critical yet challenging task, especially when buildings collapse and access is restricted. Traditional ground-based search and rescue operations are often delayed due to unstable structures, debris, and hazardous conditions. To overcome these challenges, Unmanned Aerial Vehicles (UAVs) have emerged as an essential technology for earthquake response. Equipped with high-resolution cameras, thermal imaging, and AI-driven analytics, UAVs can swiftly scan disaster zones, identify survivors, and provide first responders with real-time data to make informed decisions.

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How UAVs Work in Earthquake Disaster Management

Modern UAV systems are built with a combination of hardware and software components that enable them to effectively assist in disaster management. The flight controller unit is responsible for managing the drone’s movement, while the remote controller allows operators to manually navigate it when necessary. Additionally, the absolute positioning system ensures precise location tracking, allowing drones to map affected areas accurately. UAVs are supported by ground control stations, which provide real-time monitoring and coordination, ensuring optimal performance in disaster zones. Beyond just aerial surveillance, UAVs play a vital role in disaster assessment and rescue operations. They can be used to generate 3D maps of earthquake-hit regions, helping response teams evaluate the scale of destruction and locate the most critical rescue points. Furthermore, UAVs provide visual guidance for first responders, helping them navigate through unstable structures safely. AI-powered UAVs can also be programmed to search for missing persons, detect potential hazards, and even deliver medical supplies to survivors in isolated locations. These functionalities make UAVs a game-changing technology for enhancing earthquake response efficiency and saving lives.

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Floods

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Introduction to Floods

Floods is a widespread phenomenon, affecting diverse regions across the globe. While often associated with river overflows, this perspective overlooks numerous other forms of water-related events. Coastal areas, for instance, face unique challenges from tidal fluctuations, storm-driven surges, and tsunami occurrences. For example, in Britain's historical context, these coastal inundations have played a particularly prominent role​. Expanding on traditional views, some organizations adopt a more inclusive approach. The Center for Research on the Epidemiology of Disasters (CRED), for example, considers significant water level increases in various bodies of water as flooding events​. This aligns with broader definitions that emphasize the submersion of typically dry land.

Types and causes of Floods

Natural water systems can experience dramatic surges due to a variety of intricate and interrelated factors, which differ based on the specific inundation event and geographical context. Although numerous classifications of water-based disasters exist, they can be categorized into three primary groups: river floods, flash floods, and coastal floods. Floods are also categorized as natural (river, flash, and coastal) or human-made (resulting from infrastructure failures). The 1993 Midwestern U.S. floods and 2005 New Orleans flooding exemplify human-made disasters. Paradoxically, dams can both prevent and exacerbate flooding. Environmental factors like deforestation and overgrazing contribute to flooding by increasing erosion and river sedimentation. This is evident in how deforestation in Nepal and Assam affects Bangladesh's flood patterns. Climate change is expected to amplify flood frequency and severity globally.

Coastal floods

Flash floods

River floods

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Technologies used to deal with Floods

Near real-time (NRT) information on natural disasters has become increasingly crucial for effective emergency response and minimizing impacts. The Internet of Things (IoT) plays a key role in this, providing real-time data from various sensors monitoring environmental conditions and infrastructure status. This data, combined with cloud computing tools, enables rapid situation assessment and informed decision-making during crises. Speed and accuracy in disaster response are vital for saving lives, reducing economic losses, and building resilient communities. The "Internet of Floods" (IoF)specifically refers to using IoT for near real-time flood detection. These technological advancements are transforming disaster management, offering timely and precise information to decision-makers and potentially reducing the physical and human toll of natural disasters.

Types and causes of Floods

SAR is particularly effective for extensive flood monitoring due to its capability to provide reliable data in all-weather, day-night conditions. For instance, high-resolution SAR imagery combined with LiDAR digital elevation models can accurately assess water depth in specific regions ​(Cian et al., 2018)​. Airborne platforms, like UAVs and helicopters, offer an alternative to satellite imagery, particularly when adverse weather conditions prevent satellite data collection. Terrestrial remote sensing techniques, despite their lower coverage, provide higher geometric accuracy and are useful during adverse weather conditions. Recent advancements in forecasting and data assimilation techniques enable near real-time flood prediction, allowing for accurate flood warnings just a few hours in advance ​(Dance et al., 2019)​. These techniques are crucial for minimizing damage and preparing for flood events.

Connected sensors can also be used to detect when a flooding is occurring. The variety of connected devices is continuously increasing, but we can describe some of the most representative: Smart Buoys: Monitor water levels, flow velocity, temperature, and quality in high-risk areas using sensors like bottom pressure recorders, tsunamometers, and wind-wave gauges. Water Level Sensors: IoT-based systems use ultrasonic distance sensors and pressure sensors to measure water levels in real-time, providing immediate alerts for potential floods. Smart Sewerage: Sensor-based systems monitor water levels and variations in sewer pipes, enhancing overall flood detection.

Smart cameras are also valuable for coastal zone management and forecasting tidal waves, using techniques like intensity difference, frequency, scale, background subtraction, and active contour models. They can detect wave overtopping, with systems developed for continuous image capture and automatic detection of high waves​.

Remote Sensing

Computer Vision

Internet of Things

The rapid evolution of remote sensing technologies, including satellite imagery and Synthetic Aperture Radar (SAR), has significantly enhanced the ability to monitor and respond to natural disasters, such as floods, even in data-sparse regions. These technologies involve collecting data from various platforms (terrestrial, airborne, and space-borne) using electromagnetic sensors. The collected data is then processed and analysed to create accurate flood maps. Different image processing techniques, such as supervised and unsupervised ​classification, thresholding, and change detection methods help distinguish flooded areas from non-flooded ones​.

Smart cameras, enhanced by intelligent image processing and pattern recognition algorithms, can perform complex tasks such as detecting objects and motion, measuring objects, and recognizing vehicle license plates, faces, human gestures, and behaviours. These capabilities extend to extracting spatial information from images to detect real-time water levels using various algorithms like edge direction ​(Park et al., 2009)​, pixel difference calculations ​(Yu and Hahn, 2010)​, and optical flow ​(Van Ackere et al., 2019)​. Infrared cameras enable night-time water level detection.

Smart Home: Various sensors detect indoor humidity, smoke, temperature, and air quality, identifying issues like fire or water penetration. Biosensors can also detect viruses in mosquitoes, aiding in disease mapping during floods.

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Wildfires

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Introduction to Wildfires

A Wildfire can occur either by natural causes or by human intervention. Natural causes include lightning, high temperatures combined with a lack of humidity and strong winds, as well as volcanic activity. Human intervention can be broken down into careless behaviour using flammable materials and of course arson ​(European Union, 2023)​. The main reasons for unintentional arson in the countryside are discarded cigarettes, unattended campfires ​(WFCA, 2022)​, uncontrolled burning of garbage and short circuits in various equipment.

Wildfires are categorized as follows: - Ground - Underground or Terrestrial-, which evolves in the roots of plants and dead vegetation present in the soil. Its main characteristic is the long duration and difficulty in locating it as it does not produce flame. Therefore, the environmental damage is caused to the subsoil. - Surface, which is the most common. This type of wildfire is easy to detect, and its intensity usually does not escalate. - Crown Fire, which starts from the lower layers of the ground, spreads to the highest points of the canopy and through it spreads throughout the forest, while using as fuel the materials found on the surface​. - Firebrands and Embers which are defined as burnt flying particles that flow along with gaseous combustion products and create new fires​.

In specific, "wildfire" is defined as an unplanned fire that can be caused either by natural causes or by human fault over vegetation

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Technologies Used in Fire

Technologies are an important tool related to wildfire management and concern the stages of detection, monitoring and control​. Combination of Unmanned aerial vehicles (UAVs) and AI technology can be used both in the Pre-Wildfire (Pre.W) stage, the Active-Wildfire stage (Act.W) and the Post-Wildfire (Post.W). For instance, UAVs that are equipped with thermal sensors are used for wildfire assessment. In stage Pre.W, fire scenario testing and modelling, weather conditions monitoring, and fuels of the materials present in the area are carried out via UAVs and AI. In the Act.W stage the risk is categorised, and the Wildfire mobility check is performed. In the Post.W stage damage is recorded and assessed, new evacuation plans are created, forest restoration is monitored, and illegal interventions are checked. In all stages UAVs and AI can greatly contributed to wildfire management.

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Extreme Weather Conditions

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Introduction to Extreme Weather Condition

Extreme weather phenomena are occurrences of unusually severe weather or climate conditions that can cause devastating impacts on communities and agricultural and natural ecosystems. Weather-related extreme events are often short-lived and include heat waves, freezes, heavy downpours, tornadoes, tropical cyclones and floods. Climate-related extreme events either persist longer than weather events or emerge from the accumulation of weather or climate events that persist over a longer period of time. Examples include drought resulting from long periods of below-normal precipitation or wildfire outbreaks when a prolonged dry, warm period follows an abnormally wet and productive growing season.

Extreme weather events can be grouped together as follows:

Heatwaves Extreme heat, in the form of this phenomenon that involves high temperatures lasting for several days, has become increasingly frequent and intense in the majority of the Earth's regions since 1950, according to the IPCC's report. Cold snaps Unlike heatwaves, this phenomenon, which involves several days of low temperatures, has become less frequent, according to the IPCC. However, this does not mean the end of unusual situations, such as Storm Philomena, which brought Spain to a standstill for several days. Tropical cyclones According to the IPCC, these have become more frequent over the last four decades. Furthermore, they are proving more destructive as they result in higher category hurricanes. All of this could well be related with an increase of the surface temperature of the sea. Droughts The lack of rain in certain areas of the world, such as the Horn of Africa, is increasingly pronounced, thereby prolonging this phenomenon and forcing thousands of people to emigrate and become climate refugees. In addition, water scarcity can also result in violent clashes. Torrential rains At the same time as there may be less general rainfall due to climate change, the rain which does fall might become more intense, resulting in typhoons capable of causing extremely destructive flooding and swollen watercourses.

Technologies used to deal with Extreme Weather

AI for climate adaptation

Drones for climate adaptation

Observation systems

Earth observation for climate adaptation

Advanced computing for climate adaptation

Internet of Things for climate adaptation

Augmented reality and virtual reality for climate adaptation

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Epidemics

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Epidemics

Epidemics are an unexpected, often sudden, increase of a specific illness within a community or region. Pandemics are when an epidemic occurs worldwide, crossing international borders and affecting a large number of people. A number of communicable diseases can be significant health threats at the local, regional and global level and lead to epidemics or pandemics. Epidemics and pandemics can be prevented and mitigated through a range of household and community measures, such as good hygiene, social distancing and vaccination.

Types of epidemics

Cholera

Influenza (seasonal)

Coronavirus

Mosquito-borne diseases

Technologies used to deal with Epidemics

Artificial intelligence

Blockchain

Open-source technologies

Nanotechnology

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Earthquake Early Warning (EEW)

A strategy developed to send alerts ahead of earthquake or tsunami events to reduce disaster impacts in many sectors of society. EEW typically involves the detection of an event when the earthquake has nucleated to provide detectable ground motion. The concept is based on the fact that S-waves and surface waves (i.e. more destructive types of seismic waves) propagate slower than P-waves (less destructive). Based on the gathered analysis alerts are being communicated to the authorities seconds or minutes before the earthquake strikes to take necessary actions such as evacuating hazardous buildings. Conventional EEW systems utilize traditional seismic instruments (i.e. high-quality seismometers) ​(G. Cremen and C. Galasso, 2020)​. EEW have been developed in several earthquake-prone countries including Mexico, Japan, Turkey, Romania, China, Italy, and Taiwan

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Coastal regions face flooding threats from cyclone-induced storm surges and tsunamis, with varying impacts across different areas. For example, the U.S. Gulf Coast is prone to storm surges, while California is more vulnerable to tsunamis. Cyclones follow seasonal patterns, but tsunamis can occur year-round. Storm surges are particularly deadly during cyclones. Tidal flooding affects estuarine areas twice daily, bringing salt water inland and harming crops. Rainfall floods, caused by intense precipitation, impact river basins and floodplains, often worsened by poor drainage and urban development.

Geochemical Sensors

Studies suggest that radon gas emissions may be a precursor of seismic activity. The implementation of geochemical sensors to monitor changes in gas emissions and other geochemical data has been implemented in a few studies and research facilities

Infrasound Technology

This technology defines sounds that fall below audible frequencies ranging from 0.003 to 20 Hz are as infrasound. Displacement of earth’s surface or raptures may be considered as a source of natural infrasound since is produced by the low frequency oscillation of the earth’s surface at the epicentre and the surrounding regions. Earthquakes with magnitude greater than 5.5 mb (Body wave magnitude) can produce infrasound waves which can be detected and recorded using infrasound sensors. Japan developed a network of 30 KUT infrasound sensors which are comprehensive sensors integrating an accelerometer, a barometer, and a microphone for detecting infrasound. Studies suggest that analysis of recorded earthquake infrasound waveforms can provide information with regards to the seismic magnitude and duration

Seismometers:

One of the most basic devices used in seismological studies. The instrumentation system involves a ground-motion sensor used to measure ground displacement in XYZ directions and a recording system to graph the waveform corresponding to the seismic wave. The waveform provides critical properties such as amplitude and frequency range of seismic signals. Such signals can be extremely dynamic with an amplitude range between 0,1nm and 10m while the frequency range is between 0.00002 Hz up to 1000 Hz​​. Since the seismograph needs to be able to capture seismic waves within this dynamic range, they are very sensitive devices which implies that the recorded wave usually can also involve other natural environmental noises such as noise from wind, ocean waves, or other weather-related activities, or minor seismic activity, and anthropogenic noise from traffic, industrial operations

+190 Countries – The Global Seismic AI Network

AI-driven seismic monitoring is revolutionizing disaster response in over 190 countries. By analyzing vast datasets in real-time, AI can predict aftershocks, enhance early warnings, and reduce earthquake-related casualties. Machine learning models process seismic data, helping governments and emergency responders take proactive measures.

AI-Powered Earthquake Prediction Accuracy

Machine learning models analyzing satellite data and weather patterns can predict flood-prone areas with 70% accuracy. This allows authorities to issue early warnings, optimize evacuation plans, and reduce disaster impact. AI-driven flood forecasting is a crucial step toward climate resilience and disaster preparedness.

Machine Learning (ML) Techniques for prediction

The ability of ML techniques to explore hidden data patterns, demonstrate a promising potential for earthquake prediction. Rule-based methods, shallow machine learning, and deep learning algorithms have already been implemented in several studies to facilitate earthquake prediction. Earthquake prediction relies extensively on historical data taken from the sensors. This data is used to train the ML models in order to classify accurately earthquake signals. Since ML models require extensive data to be trained, the enhancement of prediction accuracy and improvement of earthquake ML models is limited due to the to unavailability of scarce historical earthquake data, since major earthquakes are not very frequent.

Observation systems

Systems usually involve and combine several technologies and a network of sensor arrays, including seismometers, accelerometers, GPS and GNNS receivers, and infrasound sensors

River Floods

River floods primarily result from the overflow of riverbanks due to heavy rainfall in major river basins. When these basins span multiple countries, upstream rainfall can cause downstream flooding. In Bangladesh, for example, heavy rainfall in the Ganges, Brahmaputra, and Meghna (GBM) basins often leads to significant flooding, despite Bangladesh containing only 8% of these basins' total area ​(Paul, 2020)​. In North America, floods frequently occur due to excessive rainfall from severe summer storms in the Midwest, influenced by the jet stream's position. Ice jams and snowmelt in the mountains also contribute to flooding. In South Asia, monsoon floods in the GBM basins, exacerbated by simultaneous heavy rainfall and snowmelt in the Himalayas, cause river overflows that flood adjacent lands. Factors like low river gradients, siltation, inadequate dredging, and disrupted drainage systems worsen the situation. Unplanned urbanization and changes in land use also increase flood risks. Additionally, constructing embankments, dikes, and other flood-control structures along major rivers can reduce their storage capacity, leading to higher flood peaks downstream. For instance, the opening of the Farakka barrage in India has been blamed for severe floods in Bangladesh, including a devastating flood in 1998.

Global Navigation Satellite System (GNNS)

This stystem utilizes a constellation of satellites to determine precise location and time information globally. The most well-known examples are GPS, GLONASS, Galileo, BeiDou. The satellites transmit microwave signals which are received by land-based antennas and receivers to obtain the position of the antenna. GNNS allows scientists to retrieve real-time positioning streams as continuous time series, and ultimately recover ground position, ground displacement, velocity, and static displacement​.The advantage of GNSS solutions over traditional seismographs is that they do not saturate with magnitude and the direct extraction of displacement waveforms, the cover of out-of-network events and the characterization of faults and slip distributions. However, to provide useful information, earthquakes must be fairly strong with a magnitude greater than 7.

Flash floods

Flash floods are sudden, intense, and localized flooding events typically caused by heavy rainfall in a short time. They commonly occur in desert and mountainous regions, steep canyons, urban areas, and small river courses. Other causes include dam failures, ice jam releases, and slow-moving thunderstorms. These floods are characterized by their rapid onset, often at night, and their violent nature. Despite affecting relatively small areas, they pose a significant threat to life and can cause severe damage to property and infrastructure. Flash floods can move boulders, uproot trees, destroy buildings and bridges, and trigger mudslides in mountainous areas. Flash floods are particularly dangerous due to their sudden occurrence, which leaves little to no time for warning. They are especially severe in arid and semiarid regions due to lack of vegetation and high erosion rates. Notable examples include a 1954 flood in Iran that killed nearly 2,000 people and the 1976 Big Thompson Canyon flood in Colorado that resulted in 140 deaths.

Extreme weather predictions

Artificial intelligence (AI) is revolutionizing extreme weather forecasting. By analyzing vast datasets, AI models can now predict hurricanes, floods, heatwaves, and wildfires with increasing accuracy, allowing for early warnings and proactive disaster response. Governments and researchers worldwide are leveraging AI-powered climate models to enhance resilience against extreme weather events.

Accelerometers

These sensors measure the velocity of a single point on the ground and provide extra information about the intensity and forces subjected to the object from ground shaking

AI for climate adaptation

Weather and climate models that are significantly more sophisticated and precise are being developed with artificial intelligence. For example, AI has added sea surface temperature data into ocean models – something human researchers couldn’t do. This has advanced the science community’s understanding of ocean current speed. Other climate adaptation advances using AI include smart sewer systems that avert flooding during heavy rainfall and drought-resistant crops.

Augmented reality and virtual reality for climate adaptation

Augmented reality (AR) and virtual reality (VR) are technologies that provide immersive experiences. This includes superimposing digital features on physical environments or using hardware such as headsets to fully immerse users. AR and VR are increasingly being used to change our behaviour around climate action and adaptation. By simulating the impacts of climate change, for example, VR headsets can show users a world with climate impacts such as changed weather patterns and biodiversity loss.

Advanced computing for climate adaptation

Advanced computing involves using highly powerful computers with enhanced accuracy and speed. These include supercomputers – the world’s biggest and most powerful computers – and quantum computers, which use subatomic particles like photons – particles of light – to perform multiple calculations at once. Quantum computing is expected to advance climate modelling and climate adaptation because it can predict processes that are essential to weather forecasting, like fluid dynamics. This is difficult for traditional computers. Supercomputing is also being made more widely available to help with weather and climate modelling.

Internet of Things for climate adaptation

The Internet of Things (IoT) is the world of connected devices that talk to each other. These might be sensors or hand-held devices that share data and monitor systems. IoT technology is being used to gather and share new kinds of data, such as changes in air quality and temperature. For instance, sensors that detect wildfires can send mobile phone alerts to people in the affected area. California-based company PanoAI uses an IoT-based platform to detect wildfires and pass information to fire professionals and emergency services. The system, which combines powerful cameras with multiple data feeds, monitors more than 5 million acres of land and detects thousands of fires.

Earth observation for climate adaptation

Earth observation uses satellites and other remote-sensing technology, or location-based techniques like weather stations, to gather information about changes on Earth. Huge volumes of satellite data are helping scientists develop new ways of managing planetary resources. For example, European Space Agency satellites have revealed new knowledge about Earth's climate, including ice melt and freshwater resources. Earth observation is also critical for early warning systems in a climate adaptation scenario, for example by spotting hurricanes before they happen.

Drones for climate adaptation

Drones – or unmanned aerial vehicles (UAVs) – are unpiloted aircraft that can be equipped with advanced cameras and cover large distances. They can also carry sophisticated equipment, like sensors to detect anomalies, and geo-positioning systems for highly precise location tracking. Drones can help organizations adapt to climate change by collecting visual data on climate risk and impacts. For example, a business might use drones to monitor water sources that are critical to its operations. Drones can also help in search-and-rescue situations after a climate disaster, for example by identifying affected communities in hard-to-reach areas.

Cholera

Cholera is an acute diarrhoeal infection caused by the bacterium Vibrio cholerae, infecting people most often via contaminated water or food ingestion. Every year, cholera causes an estimated 3 to 5 million cases and 100 000 to 120 000 deaths. The short incubation period (12 hours to 5 days) enhances the potentially explosive pattern of outbreaks. Cholera is an extremely virulent disease and causes acute watery diarrhoea in both children and adults. Although three quarters of all patients do not show any symptoms although the bacteria are present in their faeces for 1–10 days after infection and are shed back into the environment, potentially infecting other people. Cholera can kill, especially those with weakened immune systems within hours if left untreated. Additionally, even asymptomatic patients spread the bacteria via defaecation, which can lead to new infections.

Mosquito-borne diseases

Mosquito-borne diseases are those spread by the bite of an infected mosquito. Diseases that are spread to people by mosquitoes include Zika virus, West Nile virus, Chikungunya virus, dengue, and malaria.

Internet of Things for climate adaptation

The Internet of Things (IoT) is the world of connected devices that talk to each other. These might be sensors or hand-held devices that share data and monitor systems. IoT technology is being used to gather and share new kinds of data, such as changes in air quality and temperature. For instance, sensors that detect wildfires can send mobile phone alerts to people in the affected area. California-based company PanoAI uses an IoT-based platform to detect wildfires and pass information to fire professionals and emergency services. The system, which combines powerful cameras with multiple data feeds, monitors more than 5 million acres of land and detects thousands of fires.

Influenza (seasonal)

Seasonal influenza is an acute respiratory infection caused by influenza viruses, which circulate in all parts of the world and can affect people in any age group. The virus particles are transmitted easily from person to person via respiratory droplets and small particles produced when infected people cough or sneeze. Seasonal influenza causes illnesses that range in severity and sometimes lead to hospitalization and death. Most people recover from fever and other symptoms within a week without requiring medical attention. However, influenza can cause severe illness or death, particularly among high risk groups including the very young, the elderly, pregnant women, health workers, and those with serious medical conditions. It is characterized by a sudden onset of fever, cough (usually dry), headache, muscle and joint pain, severe malaise (feeling unwell), sore throat and a runny nose. The cough can be severe and last two or more weeks. The time from infection to illness is about two days.

Nanotechnology

Covid-19 is spreading rapidly over the globe, but there are few specific tools available to control the growing pandemic and to treat those who are sick. Quarantine, isolation, and infection-control measures are all that can be used to prevent the spread of the disease and those who become ill must rely on supportive care. What is lacking is a specific antiviral agent to treat the infected and subsequently, decrease viral shedding and transmission. Νano-based products are currently being developed and deployed for the containment, diagnosis and treatment of Covid-19. An experimental nano-vaccine has become the first vaccine to be tested in a human trial.

Open-source technologies

During disease outbreaks, rapid data sharing is critical as it allows for a better understanding of the origins and spread of the infection and can serve as a basis for effective prevention, treatment and care. The capacity of information technologies to allow for low-cost dissemination and collaboration of data have led to the establishment of a multitude of repositories and information technology platforms for data sharing. Most of these data-collection activities are coordinated by international organisations such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control. At the same time, an increasing number of bottom-up, open-data initiatives and open-source projects have also been developed, facilitating access to research data and scientific publications as well as sharing blueprints for production of critical medical equipment such as ventilators and face shields.

Artificial intelligence

Analytics have changed the way disease outbreaks are tracked and managed, thereby saving lives. The international community is currently focused on the 2019-2020 novel coronavirus (Covid-19) pandemic, first identified in Wuhan, China. As it spreads, raising fears of a worldwide lockdown, international organisations and scientists have been using artificial intelligence (AI) to track the epidemic in real-time, so as to be able to predict where the virus might appear next and develop an effective response.

Blockchain

Covid-19's highly infectious nature means that there is a pressing need to find appropriate solutions, from speeding up the detection of virus carriers and halting the spread of the virus to developing a vaccine. Blockchain technology has recently emerged as a key technology in the critical domain of epidemic management. Blockchain applications could provide a robust, transparent and cheap means of facilitating effective decision-making and, as a result, could lead to faster responses during emergencies of this kind. In the context of this pandemic, blockchain has the potential to become an integral part of the global response to coronavirus by tracking the spread of the disease, managing insurance payments and maintaining the sustainability of medical supply chains and donation tracking pathways.