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An overview on AI in disaster response

February 12, 2024

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This article has been written by Surabhi Shukla pursuing a Personal Branding Program for Corporate Leaders from Skill Arbitrage.

This article has been edited and published by Shashwat Kaushik.

Introduction

Artificial intelligence, usually known as AI, is a field that imitates human intelligence in computer systems and machines. It actually involves the development of software, hardware and algorithms that enable the machines to perform the tasks that, in the traditional sense, require human intelligence. These artificial intelligence systems aim to replicate cognitive functions such as problem solving, perception, learning, reasoning and language comprehension. The AI enables the machines to process and interpret data, make decisions and adapt to new situations without explicit human programming. Artificial intelligence has the ability to merge data from multiple sources and analyse it as a single stream that provides predictive insight.

As AI technology is continuously advancing, it is very much expected that proactive analytics will become more accessible, available and widely used. This means that before any disaster occurs, AI will hold the promise of accurately predicting it. Enabling the timely implementation of all the early warnings and preventive measures.

AI for disaster response is a platform to perform automatic classification of crisis-related microblog communication. AI enables humans and machines to work together to apply human intelligence to large-scale data at high speed. Artificial intelligence plays a pivotal role in disaster response, increasing the efficiency, accuracy and speed of various tasks during and after natural or man-made disasters. Timely disaster response requires up-to-date damage assessment to prioritise rescue and restoration resources. Artificial intelligence applications, such as geospatial analysis, remote sensing techniques, tracking and mapping, machine learning, robotics, drone technology, telecom and network services, accident and hot spot analysis, environmental impact analysis, smart city urban planning, and transportation planning, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters.

A constructive overview of AI in disaster response

Risk assessment

AI algorithms are invaluable for assessing the vulnerability of infrastructure, communities and ecosystems to various hazards. This helps with disaster preparedness planning. Remote sensing technologies and Geographic Information Systems ( GIS ) are also used to map and analyse the high-risk areas.

Vulnerability analysis

AI-driven risk assessment models analyse a combination of historical data, geographic features and environmental conditions. This enables a more comprehensive understanding of the disaster-prone areas and helps the planners make more informed decisions regarding land use, building codes and infrastructure development.

Real-time data integration

Artificial intelligence can integrate real-time data from a variety of sources, including weather stations, remote sensors, and social media. This enables a rapid update of risk assessments during ongoing disasters, enabling more agile response strategies.

Search and rescue

AI-equipped drones and robots are being used for search and -rescue operations in the disaster-affected areas. They excel at navigating challenging terrains and identifying human presence using various different types of advanced sensors, including thermal imaging.

Drones in rescue search: Drones equipped with AI algorithms can be quickly deployed to survey disaster areas and can really be useful. They can identify the location of the survivors, assess the amount of damage done, and even deliver essential supplies to various isolated areas as well.
AI-enhanced robots: Robots with the capabilities of artificial intelligence can enter dangerous environments, such as different kinds of collapsed buildings, to find and retrieve the survivors. Sensors and imaging systems controlled by artificial intelligence allow them to work autonomously in different kinds of complex situations.

Communication and information management

Artificial intelligence plays a vital role in filtering and prioritising information to alleviate information overload and misinformation. This ensures that the emergency agencies receive the critical data immediately.

Information filtering: Artificial intelligence can filter and prioritise the incoming information, further reducing respondent information overload. This ensures that emergency agencies receive critical and actionable data, reducing the risk of misinformation.
Natural language processing: NLP tools process and understand emergency calls, various text messages and posts on social networks. They can identify distress signals, gain situational awareness and coordinate response efforts more effectively.

Early warning systems

Sensors, satellites, and artificial intelligence weather forecasting models help in predicting and detecting disasters such as hurricanes, floods, earthquakes and wildfires. The machine learning algorithms also analyse historical data to offer early warnings, allowing the authorities to prepare and evacuate the affected areas in disasters.

Weather forecasts

AI plays a key and very important role in weather forecasting. Advanced AI models can analyse historical weather data, satellite imagery and atmospheric conditions to predict the path and intensity of storms. The ability to issue accurate forecasts well in advance allows the authorities to prepare and initiate the evacuations, potentially saving many lives.

Earthquake early warning

In earthquake-prone areas, AI-driven sensors can detect initial seismic waves and provide seconds to minutes of warning before the more destructive waves arrive. This kind of brief warning can trigger automatic responses, such as shutting down critical infrastructure or sending alerts to the public.

Predictive analytics

Machine learning models are deployed to predict the spread and impact of the disasters, enabling more efficient resource allocation and decision-making by the response agencies.

Disaster modelling

Artificial intelligence models simulate various disaster scenarios to predict potential consequences. For example, in the event of a flood, models can predict which areas are likely to be flooded and the likely extent of flooding, allowing authorities to plan evacuations and allocate resources accordingly.

In addition, models can be used to assess the risk of flooding in different areas, which can help communities make informed decisions about where to build and how to prepare for flooding. For example, a model might show that a particular area is at high risk of flooding, which could lead to the area being designated as a floodplain and being subject to regulations that restrict development.

Models can also be used to study the effects of flooding on the environment, such as the impact on water quality and wildlife habitat. This information can be used to develop mitigation strategies to reduce the negative impacts of flooding.

Overall, flood models are a valuable tool for managing the risk of flooding. They can help to save lives, reduce property damage, and protect the environment.

Resource allocation

Predictive analytics helps optimise the allocation of resources such as emergency response teams, medical supplies and equipment. AI models take into account factors such as population density, topography and available infrastructure to determine the most efficient distribution of resources.

Population density: The number of people living in a given area can affect the need for resources such as water, food, and healthcare.
Topography: The physical features of an area, such as mountains, rivers, and forests, can affect the availability of resources and the ease of transporting them.
Available infrastructure: The roads, bridges, and other infrastructure in an area can affect the ability to distribute resources efficiently.

AI models can use these factors to create a detailed map of the needs of a given area. This map can then be used to develop a plan for distributing resources in a way that maximises efficiency and minimises waste. For example, an AI model might identify a region with a high population density and a lack of access to clean water. The model could then recommend a plan to build a water treatment plant in the area or to provide residents with water filters.

AI models can also be used to track the distribution of resources over time. This information can be used to identify areas where resources are being allocated inefficiently or where there is a need for additional resources.

AI models are a powerful tool that can be used to improve the efficiency and effectiveness of resource distribution. By taking into account a variety of factors, AI models can create plans that meet the needs of specific areas and populations.

Data analysis and decision support

Artificial intelligence processes and analyses vast amounts of data, including social media and sensor data, and provides real-time information to respondents. Decision support systems help authorities make critical decisions during disaster response, such as managing evacuation routes and prioritising aid distribution.

Real-time data processing

During a disaster, AI systems can process real-time data feeds from sources such as social media, remote sensors, and emergency calls. This data is then structured and analysed to provide critical information to the respondents. For example, AI systems can be used to track the movement of people and resources, identify areas of need, and predict the spread of a disaster. This information can be used to make better decisions about where to deploy resources and how to allocate aid. AI systems can also be used to provide real-time updates on the situation, which can help keep people informed and safe.

In addition to providing information to first responders, AI systems can also be used to automate certain tasks, such as triaging patients or dispatching emergency vehicles. This can free up human resources and allow them to focus on more critical tasks.

AI systems are still under development, but they have the potential to play a significant role in disaster response. By providing real-time information and automating tasks, AI systems can help save lives and reduce the impact of disasters.

Decision support system

Decision support systems with artificial intelligence help authorities make various critical decisions during the disaster response. These systems can suggest evacuation routes, resource allocation priorities, and other critical information to streamline response efforts. For example, a decision support system could use AI to analyse real-time data on the location and severity of a disaster, as well as the availability of resources, to recommend the best evacuation routes for people in the affected area. The system could also use AI to identify the most critical areas that need resources, such as medical supplies or food, and prioritise the allocation of those resources.

Decision support systems with AI can help authorities make more informed and timely decisions during the disaster response, which can save lives and reduce the damage caused by the disaster. These systems are still under development, but they have the potential to revolutionise the way that disasters are responded to.

In addition to suggesting evacuation routes and resource allocation priorities, decision support systems with AI can also be used to:

Identify areas that are at risk of flooding or other hazards.
Predict the path of a storm or other natural disaster.
Coordinate the responses of different agencies and organisations.
Monitor the progress of the response effort.
Provide feedback to authorities on the effectiveness of their decisions.

Decision support systems with AI are a valuable tool for disaster response. They can help authorities make better decisions, save lives, and reduce the damage caused by disasters.

Disaster response coordination

AI-powered software is critical to coordinating and optimising the deployment of emergency response units and resources. Chatbots and virtual assistants facilitate the dissemination of information to affected populations and resolve their queries.

Resource coordination: AI-driven software can track the location and availability of response teams, equipment and supplies. This information is then used to effectively allocate resources where they are most needed.
Virtual assistants: Chatbots and virtual assistants help to disseminate information to the affected populations and answer their questions. These AI tools provide timely and accurate information to help people make informed decisions.

Natural Language Processing (NLP)

NLP tools are the ones used to process and understand emergency calls, various text messages and different social media posts. They identify distress signals, gather situational awareness, and help coordinate response efforts.

Adaptive systems

AI systems are actually designed to adapt and learn from each response to a disaster, improving their effectiveness in subsequent events.

Continuous learning: Artificial intelligence systems learn from each disaster response and gain insight into what works and what doesn’t work. Over time, this leads to more effective disaster management strategies.
Algorithm improvement: The adaptability of artificial intelligence actually enables continuous improvement of the algorithm, making the technology much more effective at predicting, responding to, and recovering from disasters.

Post-disaster recovery

Artificial intelligence plays a key role in assessing infrastructure damage and the environmental impact of disasters, accelerating recovery efforts. Machine learning helps manage and optimise the distribution of humanitarian aid and resources to affected communities.

Damage assessment: AI technology is used to assess the damage to infrastructure, buildings and critical equipment. Drones and AI image analysis can quickly provide a comprehensive view of affected areas.
Distribution of aid: Machine learning helps manage and optimise the distribution of aid and resources to affected communities. This ensures that help is provided where it is needed the most.

Remote monitoring

IoT devices and AI-driven analytics remotely monitor the critical infrastructure, such as the bridges, the buildings and power grids, for signs of damage or instability.

Infrastructure monitoring: IoT devices equipped with various sensors can continuously monitor the structural integrity of bridges, buildings and other critical infrastructure. AI analytics can detect anomalies and issue various warnings as and when necessary.
Environmental monitoring: AI technology can also monitor environmental factors such as air quality, water level and soil condition. These data are essential for assessing the environmental impact of any disaster.

Conclusion

In short, we can sum up that AI in disaster response represents a remarkable marriage of technology and humanity’s desire to protect and help each other in times of crisis. It harnesses the power of artificial intelligence to make disaster prediction, its response and recovery more effective and efficient, ultimately saving lives and reducing the impact of catastrophic events.

An overview on AI in disaster response

Introduction

A constructive overview of AI in disaster response