AI in Disaster Management - Benefits and Use Cases

Every year, natural calamities wreak devastation on the planet. According to the National Oceanic and Atmospheric Administration, several disasters caused more than $268 billion in losses in 2020. To function successfully, relief efforts and emergency management require the well-rounded backing of AI and social media. 

 

AI and machine learning have matured to the point where they can make accurate predictions as well as perform identification and classification tasks. To stay up, national, state and local governments and organisations are dealing with how to modernise disaster management techniques. 

 

IoT, artificial intelligence (AI), and machine learning can all be beneficial. These innovations have the power to increase preparation while also lowering the human and infrastructural costs of catastrophic crises when they occur. Well understanding the benefits of Ai in disaster management will be a good start. Let’s get through it.

 

(Related read, 4 Ways of IoT Helps in Disaster Management)

 

 

Benefits of AI in Disaster Management

 

The potential for AI to assist with disaster resilience is tremendous - directing relief operations, providing optimum evacuations, and delivering supplies that might benefit tens of millions, if not hundreds of millions, of people each year. 

 

While there are obstacles to overcome, with the correct amount of cooperation and collaboration, a brighter future may be a little more within grasp.

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Benefits of AI in Disaster Management

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1. Better Communicate Across Existing Programmes

 

Enhanced communication techniques are focused on discrete use cases among a few partners, to create a more impact-focused network of AI-driven disaster response. 

 

We often find unnecessary duplication in the field of data analytics, with people working on similar use cases that might be simplified. As one strategy, one alternative may be to form a domain-specific partnership or coalition through which sector and global agencies would collaborate with targeted development teams.

 

Building Necessary Tools for the Future 

 

Rather than spending the majority of funding on very advanced AI, create more basic data collecting and coordination tools across multiple agencies on the ground in the short run. This might offer "fuel" for new life-saving algorithms in the future. 

 

As a result, while more advanced algorithms are being developed, it would be advantageous to devote an equivalent amount of development work to these core tools.

 

 

2. Domain-Specific Agreements on AI Principles

 

Additional domain-specific agreements on ethical AI standards are urgently needed. Many attempts have been launched by global organisations, including the United Nations and the European Union, to define guidelines that will govern the constructive use of AI in general. 

 

However, considering the complexity of the project, this is likely to take some time. In the meantime, it might be beneficial to closely align stakeholders in specialised areas, such as disaster response. 

 

Setting up an algorithm review procedure to guarantee AI solutions satisfy defined criteria before they are publicly distributed might be part of this.

 

(Check out - AI driven platforms)

 

 

Use Cases of Targeted Disaster Relief Management

 

In the event of a disaster, the very first step is to put together a critical reaction team to assist people in need. Before the team gets into the action, it is critical to study and assess the degree of the damage and to ensure that the appropriate help is delivered first to those in most need. 

 

AI tools such as picture identification and detection, which can analyse and view photos from satellites, can be highly useful in analysing the damage. They can quickly and efficiently filter photos that would have taken months to sort manually. 

 

These photos may be used by AI to recognise items and characteristics such as damaged buildings, water, and obstructed highways. They may also locate transient settlements, which may suggest that individuals are homeless, and so direct first aid to them.

 

Sentiment Analysis on Social Media Platforms

 

In today's world, social media platforms are a key source of news. During a tragedy, social media users provide some of the best actionable intelligence. AI can evaluate and authenticate real-time photos and comments from Twitter, Instagram, and YouTube to distinguish between true and false information. 

 

These critical statistics can assist on-the-ground assistance workers in arriving at the point of crisis sooner and directing their efforts to the most vulnerable. This information can also assist rescue teams in lowering the time it takes to locate victims. 

 

Furthermore, artificial intelligence (AI) and predictive analytics tools may scan digital information from Twitter, Facebook, and YouTube to offer advance warning, ground-level location information, and real-time incident verification.

 

 

Emergency Numbers 2.0

 

During an emergency, the initial point of contact is 911 (US) & 100 (IN). On a typical day, the emergency numbers dispatch centres are already overburdened with calls. In the event of a tragedy or crisis, the figure is quadrupled, if not more. 

 

This asks for traditional emergency centres to be supplemented with contemporary technology for improved administration. Traditional 911/emergency centres rely only on voice-based calls. To gather additional sorts of data, next-generation dispatch providers are improving their emergency dispatch systems with machine learning. 

 

As a result, they can now consume data from not only conversations but also text, video, audio, and photos in order to evaluate it and make speedy decisions. The insights gathered from all of this data may be handed on to emergency response teams on the ground, allowing them to carry out crucial activities more effectively.

 

 

Improved Handling of Distress and Help-Calls

 

In the case of a disaster, emergency relief agencies are inundated with distress and aid calls. Managing such a large number of calls manually is time-consuming and costly. It is also possible for vital information to be lost or overlooked. In such instances, AI can serve as a dispatcher 24 hours a day, seven days a week. 

 

AI systems and voice assistants can analyse large volumes of calls, assess the sort of incident that happened, and confirm the location. They could not only naturally engage with callers and manage calls, but they can even instantly transcribe and interpret languages. 

 

AI systems can detect urgency by analysing the tone of speech, filtering out redundant or even less urgent calls and sorting them depending on the seriousness of the problem.

 

(Also read, AI in Energy Sector)

 

 

Proactive Catastrophe Management with Predictive Analytics

 

Machine learning and other data science methods are not confined to supporting on-the-ground rescue teams or simply after the event has occurred. Predictive analytics, for example, may evaluate historical occurrences to discover and extract trends and populations prone to natural disasters. 

 

1. To detect at-risk locations and enhance forecasts of future occurrences, a wide range of unsupervised and supervised learning algorithms are applied. 

 

2. AI may literally assist save humanity from all types of calamities simply having too much knowledge, and even coordinating relief operations to those most vulnerable. 

 

3. AI, like any other evolving technology, will expand on its current capabilities. It has the ability to detect and remove outages before they occur with a more informed and clear image of the disaster region, thus saving lives.

 

 

AI in Disaster Management Challenges

 

There are new prospects for AI in space and associated technologies, which can also have a real-world application in catastrophe risk identification, analysis, and reduction.

 

We recognise that making sound, timely judgments is essential for avoiding, mitigating, and managing a wide range of risks. In this regard, the application of artificial intelligence (AI) in decision-making has shown enormous potential. Despite these assurances, the primary obstacles are as follows:

 

• To participate actively stockholders in all stages of the process

 

• Fostering trust to transfer usable, useful information to key actors in order to efficiently

 

• Converse the risk and its uncertainty

 

• To develop hybrid models that combine traditional statistics with behavioural traits

 

• To improve data and method ease of access and transparency.

 

 

Creating a Plan for AI Deployment

 

The application of current technology such as AI and machine learning will aid in the prediction of natural catastrophes. However, before using AI in real-world applications, it is critical to address the technology's limitations. As a result, researchers must concentrate on resolving existing challenges with AI

 

To successfully deploy AI, government organisations require a roadmap that simplifies the adoption process. The following stages are included in the roadmap for successful adoption and application:

 

• Hire competent academics and IT gurus who have dealt with artificial intelligence.

 

• Collect high-quality data for the AI application training.

 

• Enlist the assistance of qualified specialists in developing adoption tactics.

 

• Current events in the government organisation should be kept up to date.

 

• Inform government employees on AI.

 

The use of AI to forecast natural disasters would save millions of lives. Furthermore, the information evaluated by AI-powered systems can aid in understanding the scale and patterns of natural catastrophes such as floods, earthquakes, and tsunamis, which would aid in improved infrastructure development in disaster-prone areas. 

 

As a result, government institutions must use AI to forecast natural catastrophes and correctly monitor them in order to protect the safety of their inhabitants. Many startup firms are developing strategies to use AI to save lives during natural catastrophes. Using AI offers various potential benefits, making it a viable solution. 

 

Using robots, sensors, or drones can assist first responders and rescue professionals in swiftly accessing the situation as well as the degree of the damage inflicted in order to devise an appropriate action plan for rescuing trapped persons. It also helps rescue attempts be more efficient, safe, and well-coordinated.