Artificial Intelligence and IoT for earthquake prediction

By KI Communications.

The proposed integrated system architecture with multiple data sources is used for AI and ML Earthquake model prediction. Credit: Boafudi Joshua, et al

The study of earthquakes remains a major interest worldwide because they are one of the least expected natural disasters. In a new review published in the journal Artificial Intelligence in Geosciences, a team of researchers from France and Turkey explored the role of traditional tools such as seismometers and GPS in understanding earthquakes and their consequences.

“These tools have provided invaluable insights into various seismic parameters, such as ground deformation and displacement waves. However, they face several limitations, including the inability to predict earthquakes in real time, challenges related to temporal data analysis, and poor spatial coverage.” Equivalent.” explains Joshua Boafodi, lead author of the review. “Despite their historical importance, these tools struggle to distinguish between seismic signals and environmental noise.”

However, the authors point out that recent developments in artificial intelligence and the Internet of Things have significantly addressed some of these limitations. AI methodologies have proven effective in identifying complex patterns and complex relationships within historical seismic data. By leveraging artificial intelligence, unique insights into seismic patterns across diverse geological sites have been gained.

“Classical and advanced machine learning techniques have contributed to the development of powerful early warning systems and decentralized forecasting models. IoT devices have also played a crucial role by enabling seamless data transfer for real-time monitoring,” Boavodi adds.

The versatility of IoT devices improves data accessibility and storage, creating a dynamic earthquake prediction network. However, challenges such as computational complexity, data quality, and interpretability remain. A major limitation is the incorporation of raw hydrogeological measurements into AI model training.

Monitoring hydrogeological data, including pore fluid pressures and fluid flow, is often expensive. Tools such as CORKs provide in situ measurements of these parameters, but data transmission is not always real-time, unlike IoT systems.

“To address these challenges, we proposed a comprehensive approach that integrates diverse data sets, including seismic sensor data, GPS, meteorology, and the Internet of Things,” says Boafodi. “By combining these datasets, researchers can develop more robust earthquake prediction models that take into account different contributing factors.”

Specifically, the authors propose integrating IoT devices with tools such as CORKs to enable real-time transmission of hydrogeological measurements that influence earthquakes. This real-time data, combined with other data sets, can be used to create predictive AI models capable of providing real-time earthquake predictions.

More information: Joshua Boavodi et al., The Role of Artificial Intelligence and the Internet of Things in Earthquake Prediction: A Review, Artificial Intelligence in Earth Sciences (2024). doi: 10.1016/j.aiig.2024.100075

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Citation: From Data to Decisions: Artificial Intelligence and the Internet of Things for Earthquake Forecasting (2024, April 2) Retrieved April 2, 2024 from https://phys.org/news/2024-04-decisions-ai-iot-earthquake.html

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