Earthquakes have been one of the most destructive natural disasters throughout human history. These sudden events can level cities in seconds and affect the lives of thousands of people. Traditional early warning systems and rescue efforts often fall short or struggle against time. However, recent advancements in artificial intelligence (AI) technologies have sparked a glimmer of hope in the fight against earthquakes. In this article, we will discuss how AI is used before, during, and after an earthquake and what promises the future holds.
Earthquake Prediction and Early Warning Systems
Today, it is still not possible to predict the exact time of an earthquake. However, artificial intelligence is helping to take promising steps in this field. Machine learning algorithms can analyze signals such as ground movements, fault line activities, and micro tremors by working on large data sets.
For example, a research team from Stanford University developed an AI model that can detect precursor tremors before an earthquake using seismic wave data. This model analyzed thousands of previous earthquake data using deep learning algorithms successful in time series analyses, such as Long Short-Term Memory (LSTM). In Japan, AI-powered early warning systems can send alerts to people, enabling trains to stop and production in factories to be automatically halted, even if just a moment before. While measured in seconds, this is critical in reducing loss of life.
Damage Prediction and Risk Assessment
One of the most critical needs after an earthquake is to quickly assess the extent of the damage. Traditional methods rely on the physical inspections of field teams and are time-consuming. However, AI can perform damage assessments by analyzing satellite images and drone data.
In a project jointly conducted by Google and Harvard University, AI algorithms can automatically detect building collapses by working on satellite images. Convolutional Neural Networks (CNN) are often used in such models. Visual clues such as collapsed structures, cracks, and sinkholes are learned by algorithms and can be reported within seconds.
Moreover, risk analyses of cities can also be performed before an earthquake using AI. Parameters such as the age of the building stock, construction quality, soil type, and proximity to fault lines are taught to machine learning models to draw possible damage predictions. Thanks to these analyses, municipalities can plan urban transformation projects more accurately.
Search and Rescue Operations
Rescuing people trapped under rubble after earthquakes is a critical process that races against time. AI-powered robots and drones play significant roles in search and rescue efforts. For example, drones equipped with thermal cameras can detect signs of life under the rubble. These drones can map priority areas by using AI, guiding rescue teams.
Additionally, AI-powered sound analysis systems can monitor calls for help coming from beneath the rubble, providing guidance to rescue teams. Especially sound recognition systems developed using natural language processing (NLP) techniques can distinguish between human voices and background noise, allowing for location predictions.
Disaster Management and Logistic Support
In major disasters like earthquakes, the rapid and effective distribution of aid supplies is critically important. AI is also being used to optimize logistic planning in disaster areas. Advanced algorithms can optimize relief routes by considering road conditions, weather conditions, and priority needs.
In the 2023 earthquakes centered in Kahramanmaraş, logistic supports were coordinated with GPS and offline map algorithms thanks to AI-based systems developed by some startups and academic institutions in Turkey. These systems provided alternative route suggestions to inaccessible points, allowing aid teams to reach regions more quickly.
AI-Powered Earthquake Simulations
Currently, many universities and research centers are developing AI-powered earthquake simulations. These simulations work with real data and model possible scenarios, providing critical information to urban planners, engineering firms, and disaster managers.
For example, information on which ground types may lead to more destruction from a seismic shock or which regions may be affected by aftershocks can be obtained more realistically through these simulations. Models fed by deep learning are continuously being improved and are turning into decision support systems for disaster management.
Challenges and Ethical Questions
As much as AI technologies offer opportunities in combating earthquakes, they also bring challenges. Issues such as the accuracy of data, biases in models, ethical concerns, and the reliability of systems are significant. For instance, a system providing inaccurate predictions could lead to misdirection of aid resources.
Additionally, questions such as how AI systems will be integrated into decision-making mechanisms in disaster situations and how human oversight will be maintained are topics that need to be addressed. Data privacy, especially in the analysis of satellite and drone images, requires sensitivity.
Conclusion: A Hopeful Future with Artificial Intelligence
While earthquakes are unavoidable realities of nature, it is now more possible to mitigate their effects and minimize loss of life and material damage thanks to AI. From earthquake prediction to rescue operations, from risk assessments to disaster management, AI-based applications have started to integrate into our lives.
In the coming years, stronger, faster, and smarter AI systems can be developed to enhance societal resilience against earthquakes. While technology alone may not create miracles, when used correctly, it has the potential to save thousands of lives. If we are to continue living with earthquakes, why not do so in a smarter way?
To make the most efficient use of AI's potential, working with robust infrastructures is critically important. PlusClouds is ready to play a central role in this transformation.
