According to the World Meteorological Organization (WMO), by 2030, 162 million people will require humanitarian assistance during natural disasters. This will cost USD 20 billion to government exchequers.
The human population is growing at a rapid phase. According to the UN report, the population will grow to 8.6 billion by mid-2030, 9.8 billion by mid-2050, and 11.2 billion by 2100. The number of people who need help during natural disasters is expected to grow exponentially.
This blog deciphers the role of AI in disaster recovery and risk management by exploring various use cases, applications, and challenges faced by AI researchers and practitioners while striding this path.
What is Disaster Management?
Disaster Management comprises strategies and measures taken to mitigate the consequences of hazards. Disasters are both natural and human-made. Disaster management is carried out with four phases of intervention, including Mitigation, Preparedness, Response, and Recovery.
Mitigation involves proactive steps taken to minimize the impact of potential disasters. Examples of mitigation measures are building structures that can withstand earthquakes or installing fire sprinklers in strategic places to fight fire accidents.
The preparedness phase comes up with a comprehensive plan and preparation to face the challenges of disaster. In this phase, you create emergency plans, conduct drills, and stockpile supplies to face emergencies.
The Response phase involves measures taken after the disaster strikes. It includes rescuing victims, providing medical aid, etc.
The process of reinstating the affected area to its pre-disaster state after the occurrence of the disaster is called the Recovery phase. In this phase, measures like repairing damaged infrastructure, providing financial assistance to affected individuals, and implementing long-term recovery plans are done.
The Role of AI in Disaster Management
Next, let us delve into the role of AI in disaster management and risk mitigation.
In the Mitigation phase, AI can help organizations and governments identify vulnerabilities in critical infrastructure. AI technologies can even outperform traditional disaster management tools in meticulous urban planning. For instance, in Japan, organizations are utilizing Digital Twins of natural and built environments along with AI tools to visualize the impact of disasters on physical structures like power grids, roads, and airports in a simulated environment. Furthermore, some researchers have applied AI to identify traffic patterns during hurricanes helping urban planners to be more prepared during disasters. Even landslides or landscape susceptibilities are effectively identified using AI tools.
AI can help you build a robust preparedness phase as it can assist in forecasting disasters. Many organizations are employing AI tools to forecast wildfires. Moving ahead organizations like the European Centre for Medium-Range Weather Forecasts have aggregated AI algorithms developed by pioneers like Google DeepMind, Nvidia, and Huawei to build AI tools that have low computational costs and have highly accurate global metrics to detect certain extreme weather events.
AI tools can strengthen the response phase by providing situational awareness and decision support for disaster management. AI can detect clusters of disaster-affected areas by analyzing social media posts posted from such regions.
Finally, AI tools come in handy in the recovery phase as they can analyze satellite imagery of post and pre-disaster-affected regions so that the relief measures can be expedited.
Case Studies from Frontiers in the Industry
Let’s explore a few use cases around how AI in disaster management and risk mitigation is transforming response strategies. From predictive modeling to real-time data analysis, AI enables proactive planning, minimizing impacts and aiding rapid recovery in disaster-prone areas.
Natural disasters cause huge losses to lives and properties if not managed effectively. Different pioneers and practitioners have developed Disaster Management Systems that are AI-enabled to provide the best results that can be beneficial in protecting valuable human lives. Here are some of the salient ones compiled by us:
The Stanford Earthquake Detecting System (STEDS): Empowering Seismologists
Artificial Intelligence has the power to analyze vast amounts of data in no time which is almost impossible for a human resource to comprehend and make decisions. Stanford Earthquake Detecting System (STEDS) developed by a team of researchers at Stanford University is a Machine Learning model to detect earthquakes that often go unnoticed by traditional earthquake detection methods. Identifying Smaller non-damaging earthquakes helps seismologists detect seismic activity in a region so that potentially devastating earthquakes are predicted accurately preventing huge loss of life and properties.
Moreover, what has been accomplished to detect earthquakes with STEDS can be used in the future to identify the threats of other natural calamities.
Google’s AI helps Mitigate Disasters Caused by Floods:
Preventing the damage caused by natural calamities has a profound value than merely taking reactive measures. Google’s AI-based flood forecasting system utilizes machine learning and predicts the severity and location of floods. This tool combines the features of computational hydrology and machine learning to provide insights into the flow of water across the land. Also, this model uses factors such as historical flood data and terrain to produce alert messages to local communities to evacuate the place of potential floods thus saving lives and minimizing damage.
Rainforest Connection: A Startup’s Initiative to Protect Forests from Wildfires and Illegal Logging
A startup, Rainforests Connection has come up with an innovative Artificial Intelligence tool to protect forests from wildfires and illegal logging. This system works through millions of networked devices deployed in the canopy of the rainforests. These devices are equipped with microphones and powered using solar energy.
The microphones capture the sounds generated in the rainforests that may be of chainsaws, logging trucks, or even early-stage wildfires, and transmit them to cloud-based servers. AI-enablement utilizes sophisticated machine-learning algorithms that are trained to recognize acoustic signatures of chainsaws, trucks, and wildfires. These ‘guardian’ devices alert the authorities to take appropriate measures saving thousands of acres of rainforest from destruction.
Swift Response Cycle with IBM’s PAIRS Geoscope
After a disaster occurs, how quickly authorities respond plays a vital role in saving the lives of thousands of people. IBM’s Physical Analytics Integrated Data Repository & Services (PAIRS) Geoscope is developed to save lives through effective disaster response times. This system uses large and complex geospatial-temporal data extracted from various sources such as satellite images, weather data, and Internet-of-Things devices and sends them to AI-based systems that analyze these datasets to deliver efficient disaster management services.
This Geoscope can detect the changes in the vegetation cover, water bodies, and infrastructure and also provide a clear picture of the worst affected areas so that relief measures can be swiftly taken in those areas.
Challenges for AI Researchers & Practitioners in Disaster Management
While implementing AI-enabled disaster management systems various challenges are encountered. Important challenges AI researchers and practitioners should focus on are:
360 Degree Transparency
Ensuring Transparency in all Stages of AI-based Disaster management solutions is key to its success. Explainability/ Decision-making Transparency, Data Transparency, and Algorithm Transparency all play vital roles in building a reliable AI-based disaster management system.
The AI model should be in a position to explain (Explainability) why a particular decision or strategy is made so that end users are aware of the reasons behind the model’s decision. As a consequence, end users know how the algorithm arrived at a particular decision.
Next is data transparency. An AI model should document clearly the data source and data sets employed in training the model. Also, they should take all necessary steps to mitigate data bias.
Furthermore, the proposed AI model should ensure Algorithm Transparency by using open-source AI frameworks and models as far as possible. Also, providing proper documentation of the algorithms used in implementing your solutions is key to ensuring transparency.
In addition, AI disaster management service providers should also ensure User Interface Transparency through intuitive and easy-to-understand designs and visualizations.
The other factors that are key to the success of AI-based Disaster system management are:
Seamless Interoperability and Integration between AI and Disaster Management systems
The key challenge is ensuring compatibility and data sharing between AI and Disaster management systems. AI systems must seamlessly integrate with existing disaster management systems and workflows.
Real-time Decision-Making Capabilities
AI systems must swiftly process and analyze information to ensure real-time decision-making. The biggest challenge for AI algorithms is handling real-time data streams and dynamic environments.
Addressing Human-AI Collaboration
The next big challenge is ensuring Human and AI collaboration. Balancing human expertise with AI capabilities requires careful design and implementation.
Data Quality and Quantity:
Reliable and comprehensive data is crucial for training and validating AI models. However, disaster data can be fragmented, inconsistent, and sometimes unavailable.
Ethical Considerations:
AI systems must be designed and used ethically, respecting privacy, fairness, and accountability.
Role of Gen AI in Disaster Management
So far, we discussed about role of AI in natural disaster management. We have numerous use cases of Generative AI’s role in disaster planning and recovery in the Information Technology space. Generative AI is playing a major role in redefining disaster management in Healthcare and Financial sectors.
Generative AI is transforming disaster recovery planning, significantly reducing downtime and enhancing organizational resilience. By leveraging predictive analytics and automation, it empowers organizations to adopt a proactive approach to IT challenges.
At the heart of this transformation is the ability of generative AI to analyze numerous datasets, and identify patterns and anomalies to predict potential system failures. This predictive capability enables organizations to shift from reactive to proactive strategies, addressing issues before they escalate into major disruptions.
Practical applications of generative AI in disaster recovery are already making a tangible impact. Financial institutions are using AI to detect and mitigate data breaches and system outages, while healthcare organizations are employing AI to safeguard patient records and ensure the continuity of medical services.
Beyond reducing downtime, generative AI fosters the development of intelligent, self-healing IT infrastructures. By learning from past incidents, AI models refine their predictive accuracy and response strategies, enhancing an organization’s overall disaster readiness.
Moreover, the integration of generative AI promotes a culture of innovation and resilience. IT teams are encouraged to think creatively and embrace cutting-edge technologies to gain a competitive edge.
Future trends of applications of AI in disaster management
| Artificial Intelligence Development | Brief Description |
| Enhanced Predictive Modeling | Utilizing advanced AI algorithms and predicting natural calamities more accurately so that early warning systems are in place |
| Real-time Monitoring and Analysis | Rapid situation assessment through AI-powered tools that process huge amounts of real-time data from different sources (satellite imagery, IoT sensors, social media) |
| Autonomous Response Systems | Creating autonomous drones and robots for search and rescue operations, delivering aid, and assessing damage in hazardous or disaster-affected areas. |
| AI-Driven Humanitarian Logistics | Optimizing the distribution of aid and resources using AI algorithms to improve efficiency and reach affected populations more effectively. |
| Social Media Analysis for Early Warning | Mining social media data to identify early signs of potential hazards, such as unusual traffic patterns or citizen reports, and generate timely responses. |
| AI-Powered Virtual and Augmented Reality | Utilizing VR and AR for training emergency responders, simulating disaster scenarios, and enhancing public awareness and preparedness. |
| AI-Enabled Crisis Communication | Developing AI-powered tools to disseminate accurate and timely information to the public during crises, mitigating misinformation and panic. |
| Collaborative Human-AI Systems | Ensuring collaboration between AI and human experts in improving decision-making and response efforts. |
| Climate Change Adaptation and Resilience | Utilizing AI to analyze climate and predict future climate trends, and helping society in developing adaptation strategies. |
