AI in Natural Disaster Management

Hurricanes, earthquakes, wildfires, and floods are all a part of nature. However, in recent years, we have witnessed an increase in the occurrence of such natural disasters. Many have lost their homes, livelihoods, and lives due to increasingly severe and frequent natural disasters. The number of climate-related disasters has tripled in the last 30 years. According to UN reports, adapting and coping with these disasters will cost developing countries an estimated $140-300 billion annually by 2030. 

However, thanks to recent advancements in technology like AI, natural disaster management is becoming more streamlined, improving everything from predicting disasters to assisting in recovery efforts. In this article, we’ll dive deep into natural disaster management, explore how AI improves each step of the process, and AI applications that are keeping us safe.

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Using AI in Disaster Prediction for Proactive Risk Management

AI disaster prediction and early detection systems can help decrease the damage caused by natural disasters. Using these systems, local residents and first responders can be warned early to mitigate the effects of the disaster. AI tools can sift through vast amounts of data and make accurate predictions to anticipate natural disasters like hurricanes and wildfires before they happen.

AI techniques like machine learning and computer vision use deep neural networks, which are used to analyze vast datasets from various types of sources, such as satellite imagery, weather data, and historical records. These networks contain interconnected artificial neurons that can identify patterns and anomalies that lead to a potential disaster. By processing and analyzing the massive amount of collected data using tools like big data analytics, AI models can be trained to provide early warning systems and help reduce the impacts of natural disasters.

For example, AI can be used to predict earthquake aftershocks by processing the seismic data. Google and Harvard developed an AI system that analyzed data from 131,000 earthquakes and aftershocks. When tested on 30,000 earthquake events, this AI system showed greater accuracy in predicting aftershock locations compared to traditional methods. In the example of an aftershock prediction shown below, the regions predicted to experience aftershocks are marked with red color. The black dots are the locations of observed aftershocks, and the yellow line shows the faults that ruptured during the mainshock.

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AI-Powered Systems for Disaster Preparedness

Once a disaster is predicted, the next step in natural disaster management is being prepared. Human and economic losses can be prevented by AI systems that provide timely and accurate information about the risks involved in a disaster. These insights help authorities be ready for emergencies and act quickly to save lives. For instance, farmers, fishers, and foresters are some of the most at-risk communities when it comes to natural disasters, and AI solutions can help these communities avoid losses. 

Computer vision and image analytics solutions can also help monitor and track weather patterns by processing satellite images in real time. By monitoring the path of such disasters (like cyclones), areas that may be affected can be better prepared to face them. For example, NASA has used cloud computing and deep learning techniques on satellite photos to track hurricanes like Harvey and Florence. Their system outperformed standard methods by six times, allowing the hurricane to be tracked every hour rather than every six hours as with traditional methods.

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AI in Disaster Response: Enhancing Real-Time Data for Rescue Teams

Another important application of AI in natural disaster management is assisting response teams. AI systems can provide valuable real-time data, such as damage assessment and tracking the location of people in distress, to disaster response teams, helping them allocate relief resources more efficiently. They can also help accelerate the delivery of aid to people in affected areas and improve the decisions and actions of front-line relief workers.

For example, during any natural disaster, emergency helplines (like 911) get flooded with distress calls. Response teams might miss important details if all the calls are routed to human operators. AI can be used to manage high volumes of calls and messages in record time and perform multiple functions using features like speech-to-text and natural language processing (NLP). Such features can extract the context behind every emergency call to get accurate information on the precise nature of the emergency and the location of the caller.

These insights reduce call times and accelerate emergency response. The same system can be applied to social media platforms with the help of AI tools like computer vision and NLP.

How can social media platforms help in natural disaster response? During a natural disaster, social media platforms can contain crucial actionable information in the immediate aftermath. For example, text, audio, images, and video information can be posted on social media by the people in the affected area. Manually managing and processing all of this information is not easy, especially when every second is crucial. Tools like the AIDR (Artificial Intelligence Digital Response) platform can be used to reduce response delays by automatically processing social media posts. The platform analyzes text and images from tweets (or other social media platforms) to get relevant humanitarian information. Disaster response organizations can use the extracted real-time data to inform their actions and improve the efficiency of their efforts.

AI innovations can also assist rescue teams directly from the front lines. Computer vision models like Ultralytics YOLOv8 can help assess the damages done to the affected area using satellite images and methods like semantic segmentation, which classifies each pixel in an image into specific categories. For example, during the aftermath of the earthquake in Turkey, the U.S. Department of Defense used semantic segmentation and satellite imagery to identify and categorize the severity of infrastructure and building damage in disaster areas at a rate much faster than traditional detection methods. Using such methods, they accelerated the process to hours or minutes, and the control center and the rescue teams were able to respond faster in affected areas for recovery.

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AI for Natural Disaster Recovery By Providing Mental Health Support

The recovery phase after a natural disaster aims to rebuild communities and make them stronger. One of the best ways to do that is by providing mental health support to the people in affected areas. Survivors might suffer from mental health issues like Post-Traumatic Stress Disorder (PTSD) after such a disaster. Rates of PTSD after a disaster can be as high as 40%. AI tools can be used to help affected individuals. For example, Omdena is a company that provides mental health support using AI chatbots.

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AI can also assist with mental health research and support. AI systems can be used to predict mental health issues by analyzing electronic health records with the same accuracy as human experts. For example, researchers have used AI to analyze social media data and gauge stress levels in young adults after experiencing trauma. According to their studies, the same technique could also be applied to understand the mental health impact of climate crises and natural disasters, both in the short and long term.

Pros and Cons of Using AI in Disaster Management

Now that we’ve discussed how AI can influence all the different processes of natural disaster management, let’s explore some of the pros and cons involved. Here are some of the advantages of using AI for disaster management:

• Improve coordination: AI systems can be used to improve coordination between different agencies and organizations involved in disaster response, reducing delays and inefficiencies.

• Automate tasks: AI can automate repetitive tasks, such as data entry and analysis, freeing up human resources for more critical activities during a natural disaster when every second spent is critical.

• Long-term climate change adaptation: AI can help identify and assess the impacts of climate change, allowing global communities to develop long-term adaptation strategies.

Despite the many benefits, there are also limitations related to AI in natural disaster management to keep in mind. Here are some of the key challenges of AI in natural disaster management:

• High implementation costs: Developing and implementing AI systems for disaster management can be expensive, as it often involves significant investments in hardware, software, and AI expertise.

• Data privacy concerns: AI systems use geospatial satellite data, social media and helpline communications data, and more sources to assess damages and track people during a disaster. This raises privacy concerns about the non-consensual use of personal information and potential surveillance.

• Reliance on data quality: An AI model's predictions can only be as good as the quality and quantity of data it’s trained on. If AI models are trained with inaccurate data, it may lead to less effective outcomes, particularly in critical situations.

Final Thoughts on AI in Natural Disaster Management

We’ve explored how AI can help save people’s lives from a natural disaster. It helps us predict, prepare, respond, and recover from them more effectively. AI tools can predict earthquakes, track hurricanes, and improve disaster response and recovery efforts. Doing so helps minimize the impact of disasters on people and infrastructure alike. While AI has many benefits, like faster responses and better coordination, it also has some challenges. These include high costs, data privacy concerns, and the need for accurate data. When we address and account for these challenges, we can maximize the impact of AI in effectively managing natural disasters.

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