In recent years, the advancement of artificial intelligence has given many people reason to be fearful. But there’s a less scary side to AI as well.
In this post, we’ll explore how artificial intelligence can help us successfully manage disasters and improve our understanding of them in the process.
Importance Of Artificial Intelligence In Disaster Management
Disaster management is a very important thing to have in any country and now that we have the technology needed to make it better and more effective, we should definitely implement it.
By using the best AI tools on the market, we can take advantage of a number of opportunities and develop smart disaster management systems that will save thousands of lives.
AI has the potential to improve proactive rather than reactive actions for disaster risk reduction, as Andrew Harper, Special Advisor on Climate Action of the United Nations High Commissioner for Refugees (UNHCR), reminded us. AI can speed up our understanding of natural hazards by analysing large volumes of data (and images) from different sources (DRR).
Benefits Of Artificial Intelligence In Disaster Management
Natural disaster management is the process of dealing with and preventing natural disasters. Disaster risk reduction is the process of reducing the risks of disasters. Disaster risk management is the process of managing the risks of disasters. Historical data can be used to identify natural hazards.
According to Adam Fysh, Coordinator of Global Risk Analysis and Reporting Section, United Nations Office for Disaster Risk Reduction (UNISDR), communication in DRR entails asking the appropriate questions regarding who poses risks and who bears the costs of those risks.
The International Telecommunication Union and the World Meteorological Organization work together to detect extreme events and manage disaster response. The two organizations have a long history of cooperation, and their partnership is essential to coordinating international efforts to respond to natural disasters.
1. Forecasting
Forecasting is one of the most important aspects of disaster management. There are many things that can be predicted accurately through the use of advanced technologies and artificial intelligence is an excellent tool in this regard.
For example, weather forecasting has been revolutionized through the use of data mining and analysis. Another example is volcanology, where massive amounts of data are used to predict volcanic eruptions.
2. Detection
It is often very difficult to detect disasters early on, since they’re often hidden from view until it’s too late. But with the advancements in technology and artificial intelligence, we have better tools that help us understand what exactly has happened, what’s happening and what will happen next.
This understanding allows us to form an appropriate response which can help reduce the effect of disaster (for example, by enabling quick evacuation).
3. Early Warning
As Feyera Hirpa, a Senior Data Scientist at One Concern Inc., proved by testing his predicting models during the 2019 flood in Japan’s Chikuma River during Typhoon Hagibis, it is a useful compound and scalable flood prediction tool.
In order to be able to detect and forecast disasters, we have to gather massive amounts of data about them. This is not easy for many types of disasters and there are often critical gaps in our understanding.
Artificial intelligence can help fill those gaps and supplement our general understanding of these events. It can also analyze the data that already exists and extract useful patterns from this data, so that we get a better idea of what works and what doesn’t.
4. Mitigation
The next step after detection, forecasting and warning is mitigation. Mitigation involves a range of activities, from evacuation to construction engineering. Artificial intelligence can be extremely useful in these activities too.
For example, machine learning can help us predict and visualize patterns in the types of damage that are caused by various natural disasters. This understanding will improve the way we build our infrastructure and make it more resistant to specific risks involved with various disasters.
5. Response
As soon as a disaster has occurred, we need to act quickly. Unfortunately, this is often made difficult by bureaucratic delays and poor communication channels. But artificial intelligence is helping us here too.
In the case of natural disasters, machine learning can help us understand what steps we need to take and how we should do them. The World Bank has used AI and sensors to measure damage from earthquakes as it happened.
During the first hour of an earthquake, damage often increases markedly but fortunately, by the second hour it’s happening at a much slower rate.
6. Education
Many disasters are caused by human activity but often these problems have been ignored for decades without any real attempts to solve them. This complacency can lead to disastrous situations in the future.
Artificial intelligence can help us get a better understanding of how humans adapt to different types of disasters and figures out how we can design more resilient cities and infrastructure. AI has been used in this area for a long time, since early people have been using it to predict earthquakes.
7. Planning
There is no guarantee that a model will perform equally well in real-world circumstances even if it precisely fits the training dataset.
After a disaster, we also need to plan for what comes next. Science fiction movies often portray disasters as totally chaotic events, where people just panic and run around without any clear plans or guidelines on what needs to be done next.
This is not true but the best way we can prepare ourselves is by making the most of artificial intelligence. For example, machine learning can help us predict the future effects of a disaster, helping us make better plans on how to react and rebuild our lives.
8. Mitigation
As we mentioned earlier, mitigation is a very important aspect of disaster management and it’s also one of the primary uses of artificial intelligence. AI can help us build more resilient infrastructure which can be used to effectively mitigate different risks associated with natural disasters.
For example, machine learning is being used to determine earthquake risk in areas that have experienced many quakes in the past. It’s also been used to determine wildfire risk by looking at the weather conditions that precede massive wildfires.
Risks Of Artificial Intelligence In Disaster Management
Natural hazards can have a significant impact on communities and individuals. Emergency management agencies are increasingly using sentiment analysis and natural language processing to help reduce the impact of disasters. These tools can help identify early warning signs and track the progress of recovery efforts.
Helen Li, Senior AI Researcher at the China Academy of Information and Communication Technology, observed that promising prediction applications are also present in wildfire-prone areas (CAICT).
1. Loss Of Control
Artificial intelligence often comes with a lot of hype and a number of fears, but the truth is that it has the potential to be very useful in disaster management. However, there are also some risks associated with it that can lead to disaster on their own.
The most common one is losing control, since we are entirely reliant on AI for decision making in many uses cases. A recent example of this happened when a self-driving car failed to return safely from its mission and was involved in an accident.
AI can be at risk for just as much as humans are and there’s also the chance that something could go wrong with the algorithm, leading to something really bad.
2. Malfunctioning
Artificial intelligence can experience errors or malfunctions too and these can have disastrous effects in disaster management. Take for example the recent events in Boston, where a self-driving car was involved in an accident.
Although no one was seriously injured, AI also has some vulnerabilities like humans do and this accident showed that there’s still a lot that needs to be done before fully autonomous vehicles can safely roam the streets.
3. Relying On Data Mining Alone
Data mining is very useful in disaster management but it’s not as good as human intelligence. At best, it supplements our understanding of disasters and our ability to manage them and at worst, it leads to disasters because we’re relying on it too much.
For example, an AI-based algorithm might get things wrong because it failed to understand the context of a person’s statement. It might also miss important information that’s not included in the data, leading to significant errors in decision making.
4. Biases
Because of the accompanying unpredictability of the models, Nicolas Longépé, Earth Observation Data Scientist, Phi-lab Explore Office, European Space Agency (ESA/ESRIN), emphasises the capabilities of AI while also pointing out its drawbacks.
If we want to use artificial intelligence for disaster management, we need to be very careful about how we design it and what biases are built into it.
For example, an AI model shouldn’t factor in race as part of its decision making or worst case scenario, it might factor in race as a negative characteristic of a person’s character, which can have disastrous consequences.
5. AI Is New And Unregulated
Artificial intelligence is the future of the world but it’s also something that’s relatively new and not fully regulated. This means that there are not enough checks and balances to ensure that AI will be used for good purposes only.
At the moment, anyone with a computer can develop AI software and there are no regulations in place to ensure that it will be used responsibly. As a result, different companies might use artificial intelligence for their own profit-making purposes, increasing inequality and discrimination in society.
6. No One Is Responsible In An Emergency
One of the primary research goals of the European Space Agency is the detection of tropical cyclones using a visual pattern analyser on satellite photos and atmospheric clouds.
If we rely on artificial intelligence too much, we might create situations where no one is responsible when something goes wrong. Imagine a situation where the AI decides to activate itself in emergency and no one is prepared for it.
7. Not Dealing With The Human Aspect
Artificial intelligence is a very useful tool for disaster management, but we also need to think about how we can use it in a way that is beneficial to humans and not just be useful animations that respond to commands.
For example, if you combine AI with robots, they can help distribute supplies, transport people and clean up after disasters. They can also work as companions and provide people with the vital information they need during a disaster.
However, their usefulness depends on us understanding them so that they can be built with compatible programming language and speech patterns which suit our needs.
Final Note
The future of artificial intelligence in disaster management looks very bright as it offers us a great deal of opportunities for more effective responses.
For example, AI can help predict the path a hurricane will take and the best place to shelter. It can also predict where a wildfire might spread, allowing authorities to deploy resources to those areas in advance so that people and structures are protected from danger.
Last Updated on October 6, 2023 by Parina Parmar
