Any disaster can adversely disrupt the affected area, population, utility supply, etc., making it one of the most challenging situations to handle.
While you will mostly find humans deployed to facilitate disaster management measures, AI has joined them in this segment. Let’s explore 12 ways AI is used for disaster response.
Early Warning and Prediction
AI-powered early warning systems are playing a crucial role in lowering the impact of disasters by identifying and predicting them before their occurrence. It helps authorities prepare accordingly and take relevant, timely steps to minimize disaster-related damages. Google’s flood forecasting AI models are its prime examples.
These models forecast the amount of water flowing in any river and areas that can be most affected 7 days before a disaster strikes. This information can enable issuing early alerts to people, helping them evacuate at the right time.
Resource Allocation
AI has become a valuable asset in disaster response management because it can analyze vast real-time data to recommend optimal resource allocation.
Besides optimizing the allocation of food supplies, water, medical resources, etc., AI algorithms can also help prioritize tasks like rescue team deployment. It ensures that resources first reach the areas where they’re needed the most. For this reason, the healthcare sector used AI during the COVID-19 pandemic to distribute resources optimally.
Search and Rescue
Effective search and rescue operations can be complex in challenging locations, like dense forests, mountainous regions, water bodies, etc. It is where AI is teaming with rescue professionals to search for missing individuals. Various AI and ML algorithms excelling at object detection enable drones to identify potential targets in dense terrains. Platforms like SEA.AI are facilitating better search and rescue operations by detecting people in the water. All this makes it easier to spot and rescue missing individuals during and after a disaster.
Damage Assessment
Manual damage assessment is tedious and time-consuming. It is also impossible to assess precise damages in hazardous conditions after disasters.
This is where AI can be leveraged. In the wake of the Hawaii wildfires in 2023, Esri developed an AI-powered damage assessment model to speed up the assessment process. The model succeeded at rapid damage assessment with 95% accuracy at a fraction of the time. This fast assessment can help authorities decide which areas to prioritize for rescue and optimize resource allocation.
Communication and Coordination
When disasters strike, social media often becomes the direct link of contact between those affected and government authorities. If government authorities can extract social media data and analyze it in real-time to identify those affected by disasters, reaching out to them to help can become more manageable. It is precisely what AI is being used for.
Post-Disaster Recovery
Once a disaster is over, government authorities juggle multiple tasks like prioritizing reconstruction in severely affected areas, rolling out severance packages, designing reconstruction efforts, etc.
These processes demand immense time and analyzing gigantic amounts of accurate data to take further action. Fortunately, various AI applications can now speed up post-disaster recovery. For instance, deep learning algorithms facilitate faster damage assessments to decide appropriate rebuilding efforts and funds, while sensor fusion and ML algorithms are helping pinpoint hazards in damaged areas to plan further risk mitigation efforts.
Automated Evacuation Routing
When disasters like massive fires break out, the first responsibility of rescue teams is to evacuate trapped people to prevent mass casualties. The traditional approaches to evacuation route determination have poor stability and low efficiency. Hence, there is growing reliance on AI technology to recommend faster and safer evacuation routes during disasters. Radar-assisted SARSA (RSARSA) is one novel algorithm that swiftly decides a safer evacuation route for crowds at disaster-struck locations, especially at arbitrary locations.
Predictive Modeling
One of the best ways to prevent major man-made disasters is by proactively assessing risks in target areas and implementing the best preventive measures to lower their occurrence chances.
This is why the Korean government is digitizing its underground facilities and information to prevent underground safety accidents. An AI-powered CRPM (Collapse Risk Prediction Model) has also been developed to provide risk warnings using 3D underground space information effectively. It can lower disasters and save more lives through early warnings.
Strengthening Preparedness
Most disasters occur abruptly and cause massive damage to the area’s properties, people, etc. While no one can stop every disaster, training rescue teams for even the most challenging scenarios is possible so they’re prepared to navigate any situation.
Rescue teams can be trained using platforms that combine AI with immersive virtual reality systems that mimic real-world environments. It ensures trainees can make mistakes in a safe yet hyper-realistic environment, learn from them, and perfect their skills for more effective rescue operations during disasters.
Medical Assistance
Healthcare professionals can find it challenging to provide rapid assistance to people affected by disasters in remote areas. This delayed medical assistance can lead to significant health deterioration and even death in some cases.
Such instances can be prevented by utilizing AI-powered telemedicine. Telemedicine lets doctors consult patients remotely, eliminating geographical barriers. AI-driven telemedicine can also conduct faster diagnoses so doctors can recommend personalized treatments for faster recovery.
Disease Outbreak Management
The risk of disease outbreaks remains higher in post-disaster scenarios. The situation can worsen if timely preventive measures aren’t taken to track potential disease outbreaks and containment.
Authorities can use machine learning algorithms to assess real-time data to identify daily exposure behavior, separate risk groups, predict epidemics, and take appropriate preventive measures.
Crisis Mapping
Whenever a disaster occurs, the first logical step should be assessing the damage magnitude and affected people to plan better emergency response efforts. It is what crisis mapping, a.k.a. disaster mapping, helps with.
It can evaluate, record, and communicate data on the disaster-affected location. The potential effects of the disaster can also be predicted. This information can help authorities strengthen their preparedness and deploy optimized rescue and relief operations.
