AI in Disaster Management: AI’s Role in Disaster Risk Reduction

Disaster Prediction
Source: Shutterstock/metamorworks
Table of Contents

Artificial intelligence (AI), with a focus on machine learning (ML), is increasingly assuming a vital role in disaster risk reduction (DRR). It encompasses various aspects such as predicting extreme events, developing hazard maps, real-time event detection, providing situational awareness, facilitating decision support, and more.

With AI’s growing presence in this domain, it raises numerous inquiries: What possibilities does AI in disaster management bring forth? What obstacles do we face? How can we tackle these challenges and capitalize on the opportunities?

Furthermore, how can we employ AI to furnish crucial information to policymakers, stakeholders, and the public, all aimed at minimizing disaster risks?

To fully tap into the power of AI for DRR and create a robust strategy, it is crucial to closely examine these questions and cultivate collaborations that propel AI in DRR to unprecedented levels.

Video source: YouTube/Complex AI

The Use of AI in Disaster Management

Given the growing instability of our climate and the rising occurrence of natural disasters, the contribution of AI in disaster management is not merely advantageous – it is indispensable. The AI application in forecasting, mitigating, and responding to disasters has brought significant changes. 

AI’s capacity to analyze large volumes of data, identify trends, and generate forecasts has been used to predict a range of natural disasters, including hurricanes and wildfires.

In the following sections, we will take a closer look at how AI is used in disaster management, spotlighting particular instances that underscore this technology’s potential.

Disaster Prediction

One of the most compelling applications of AI in disaster management lies in seismology, where it is being used to predict one of the most devastating natural disasters known to humankind: earthquakes.

For example, the Stanford Earthquake Detecting System (STEDS), an AI model developed by Stanford researchers, was developed for earthquake prediction to identify minor quakes often overlooked by conventional methods.

These small tremors offer crucial data about a region’s seismic activity, aiding in forecasting larger, more destructive earthquakes. STEDS’ success underscores AI’s capacity in disaster prediction and sets a framework for its use in predicting various natural disasters. 

Disaster Prevention

AI has emerged as a crucial ally in disaster prevention, acting as a vigilant guardian that continuously observes a wide range of elements, from subtle shifts in weather patterns to changes in geological formations, to anticipate potential disasters.

This enables the implementation of proactive strategies, such as evacuating populations from areas predicted to be affected by hurricanes or strengthening infrastructure in earthquake-prone regions.

A notable example of AI’s application in disaster prevention is Google’s flood forecasting system, currently operational in Bangladesh and India.

The system employs a blend of computational hydrology and ML to model the flow of water across the landscape, considering factors like terrain and historical flood data. Subsequently, it produces maps and alerts shared with local communities and authorities, granting them vital time to prepare and respond.

Video source: YouTube/Google

Disaster Response

In the face of escalating natural disasters, the need for swift and effective responses is critical. In that sense, AI, with its ability to process vast data rapidly and accurately is turning it from a traditionally reactive process to a proactive, data-driven approach.

IBM’s PAIRS Geoscope, a unique cloud-based geospatial analytics system, exemplifies this. It uses AI to analyze satellite images and assess disaster damage.

This real-time information allows disaster response teams to prioritize areas needing immediate attention and plan accordingly, significantly improving their efficiency and effectiveness. The system also aids in recovery planning by identifying areas at risk of future disasters. 

Advantages of AI in Disaster Management

AI has become an indispensable asset in disaster management, especially in light of the increasing frequency and ferocity of natural disasters attributed to climate change.

The advantages of AI in disaster management are diverse, spanning from the enhancement of early warning systems to support post-disaster recovery.

Let’s take a closer look at some of the benefits of AI in disaster management:

AI in Enhancing Early Warning Systems

With their advanced pattern recognition capabilities, artificial intelligence algorithms play a crucial role in predicting potential natural catastrophes. These algorithms can sift through vast amounts of data, identifying patterns and trends that could indicate an imminent disaster.

Specifically, AI-based systems can meticulously analyze satellite images to spot early indications of natural disasters. They can detect the initial formation of hurricanes or the telltale signs of wildfires.

This early detection is critical as it gives authorities the necessary lead time to issue warnings. It also allows for the prompt evacuation of areas that are at risk, thereby potentially saving lives and reducing property damage.

AI for Accurate Weather Forecasting

AI can enhance traditional weather forecasting models, which depend on complex mathematical equations to mimic atmospheric behavior. 

It does this by adding more data sources and using machine learning techniques. These techniques can identify patterns that human forecasters might miss.

In turn, it leads to more accurate and timely predictions, enabling better preparation and response to natural disasters.

AI in Disaster Response and Recovery Efforts

During a natural disaster, AI-powered drones and robots, as the latest technology in disaster management, can be deployed to assess damage, search for survivors, and deliver aid to affected areas.

These autonomous systems can operate in hazardous environments that may be too dangerous for human responders, such as areas affected by radiation or toxic chemicals.

Additionally, AI can assist in optimizing resource allocation during disaster response efforts by analyzing data on the severity of the damage, the needs of affected populations, and the availability of resources.

AI in Post-Disaster Recovery and Rebuilding

Following a natural disaster, AI can aid in long-term recovery and rebuilding efforts.

AI algorithms can identify the most effective strategies for rebuilding infrastructure, restoring ecosystems, and supporting affected communities by analyzing data on past disasters’ impacts.

This ensures that recovery efforts are targeted and efficient, thereby minimizing the long-term consequences of natural disasters.

Video source: YouTube/Jail Busters

Challenges of AI in Disaster Management

Although AI has made significant strides in managing natural disaster risk, its limitations have hindered its widespread application in real-world scenarios.

Here are several challenges of AI in disaster management that need to be addressed:

Data Collection and Handling Challenges

One of the primary challenges lies in the collection and handling of data. Biases in training and testing datasets can significantly impact the performance of AI models.

For instance, building a representative dataset containing examples of extreme events, which are inherently rare, is a considerable challenge.

Moreover, integrating new distributed AI technologies within the data domain and ethical issues surrounding data handling further complicate the process.

Computational and Transparency Issues in AI Models

AI models often rely on complex structures, making them computationally expensive to train. This large and expensive computing capacity requirement is not always accessible, posing another challenge to AI’s application in DRR.

Furthermore, the “black box” predicament, where the decision-making process of AI models is not easily understandable, can hinder trust in these models.

Operational Implementation Challenges

Operational implementation of AI models also presents challenges, particularly in terms of user notification.

AI model outputs need to be translated and visualized according to end-user needs, requiring the inclusion of various stakeholders in the design and evaluation of alert and early warning systems, forecasts, hazard maps, and decision support systems.

Informed decision-making needs to present the confidence levels, uncertainties, and constraints of a system enhanced by AI in an easily comprehensible manner.

Efforts to Facilitate AI for Disaster Risk Reduction

Despite these challenges, efforts are being made to facilitate the use of AI for DRR.

These include the following:

  • Supporting greater data availability,
  • Offering resources and software kits to facilitate the development of AI,
  • Enhancing model explainability,
  • Offering new applications for AI-based methods,
  • Contributing to the development of standards.

A significant contribution to these efforts is the establishment of the Focus Group on AI for Natural Disaster Management (FG-AI4NDM) by three UN agencies: the World Meteorological Organization, the International Telecommunication Union (ITU), and the United Nations Environment Programme.

This group is exploring how AI can enhance comprehension of natural disasters and support disaster relief efforts and early warning systems on a global level. The FG-AI4NDM is working on best practices for AI use in data collection, improved modeling, and effective communication. 

Still, the full potential of AI in disaster management remains unrealized until these challenges are effectively addressed.

AI in Weather Forecasting: A Game-Changer or Not?

AI in weather forecasting, a crucial part of disaster prediction, offers faster, cheaper, and potentially more accurate predictions than traditional models. However, the integration of AI into daily forecasting and its acceptance among meteorologists is still under discussion. 

AI’s potential has been demonstrated in various forecasting tasks, leading to increased recognition of its capabilities. A new generation of AI models, primarily developed by the private sector, is now matching or even surpassing traditional models.

The rapid development of AI models, especially those capable of global forecasting, is attracting attention from leading government forecast centers like the European Centre for Medium-Range Weather Forecasts and  National Oceanic and Atmospheric Administration (NOAA).

Whether AI models will become the primary tools for meteorologists in disaster management hinges on the ease of transitioning these models into operations and their acceptance by government agency leaders and forecasters.

This is particularly important as, despite advancements in weather model accuracy, predicting extreme weather hazards remains a challenge. These events require detailed, confident, and timely forecasts for adequate preparation.

AI’s innovative approach to forecasting natural disasters uses vast amounts of historical data to find relationships between past conditions and future outcomes. AI models can generate forecasts in minutes, a significant improvement over the hour or more required by most conventional models.

AI’s speed and efficiency could enable ensemble modeling, providing a range of possible outcomes rather than a single forecast. This could be particularly beneficial for subseasonal-to-seasonal forecasts, which predict weather trends weeks to months in advance and can be crucial in disaster management.

However, despite AI’s advancements, the human element in forecasting will always be crucial. This is especially true in complex emergencies or severe situations where timeliness, accuracy, actionability, and trust are paramount.

AI in Disaster Management: Key Takeaways

Natural disasters, driven by climate change, are becoming more frequent and intense, leading to escalating humanitarian crises.

Traditional methods of predicting these disasters, such as satellites and other meteorological infrastructures, provide valuable information. However, the integration of AI in disaster management can significantly enhance these methods. 

AI offers several advantages in disaster risk reduction. These include:

  • Enhancing early warning systems,
  • Accurate weather forecasting,
  • Disaster response and recovery efforts,
  • Post-disaster recovery and rebuilding.

Incorporating AI in disaster management can significantly enhance our ability to predict, prepare for, and respond to natural disasters.

As we face the increasing challenges posed by climate change, these technologies offer a glimmer of hope, providing us with the tools to better protect our communities and mitigate the impact of these devastating events, offering a more resilient future in the face of climate change-induced disasters.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.