Tibet Tragedy Shows AI Can&#8217;t Predict Earthquakes—Yet
On January 7, a powerful earthquake struck Tibet near Mount Everest, claiming at least 126 lives and leaving over 180 injured. Chinese authorities measured the quake at a magnitude of 6.8, while the US Geological Survey measured it at 7.1. The earthquake sent tremors across Nepal, Bhutan, and parts of India. Yet, amidst the significant loss and damage, a critical question arises: Could this disaster have been foreseen? In 2023, during a seven-month AI trial in China, researchers from The University of Texas at Austin collaborated with Chinese scientists to report a 70% success rate in predicting earthquakes a week in advance. Such advancements showcased AI’s potential to mitigate disaster risks. However, the absence of advance warnings for the deadly earthquake in Tibet raises pressing concerns about the current limitations of these technologies. Research Papers Claim to Predict Earthquake “If someone claims they can predict earthquakes, they are misguided,” M. Ravichandran, Secretary, Ministry of Earth Sciences (MoES) told AIM. “No one in the world is currently able to predict earthquakes. Although progress is being made in this direction, a reliable system is not yet in place even here in India.” Elaborating on this, Munish Bhatia, an assistant professor at NIT Kurukshetra and co-author of a research paper on AI-based earthquake prediction, explained the complexity of the task to AIM. “Predicting earthquakes with precision is highly challenging due to the dynamic nature of seismic activity and tectonic plate movements. While analysing parameters like seismic activity and energy release allow for certain predictions, the Earth’s crust is too complex to fully decode.” Bhatia mentioned that, in the study, they developed a mathematical model that incorporates various parameters, including time-domain data, spectral conditions, energy release, and entropy. Yet, he pointed out that accuracy depends heavily on the quality and scope of input data. Controlled experiments may yield promising results, but real-world applications face significantly more variability and unpredictability. Speaking on other research papers published on the same lines, Bhatia explained, “The prediction model’s accuracy can vary significantly depending on the number and type of parameters and variables incorporated. It also depends on the scenario in which the experiments were conducted, the type of simulation models used, and the conditions under which they were developed.” For example, achieving a 70% prediction accuracy is not exceptionally high. Instead, it only represents a probability and depends on the quality of the model and the data used. “To improve accuracy, it is crucial to train models on large datasets, including historical data about earthquake occurrences in specific regions. Regional history, entropy variations, and space-time graph analysis are critical factors. In our experiments, we worked within a highly controlled environment, which contributed to achieving better accuracy. However, if additional parameters are introduced, the model’s accuracy could decrease due to increased complexity,” Bhatia added. How Close are India’s Predictions? As countries prepare to tackle disasters, India has also taken steps to ensure human safety in such situations. In an exclusive conversation with AIM, GS Srinivasa Reddy, former director of the Karnataka State Natural Disaster Monitoring Centre (KSNDMC), explained, “At present, India does not have systems for earthquake prediction, even at the central government level. While the National Center for Seismology (NCS) is in the process of developing AI-based technologies, we are still far from achieving reliable earthquake forecasting.” “For instance, Japan employs advanced technologies such as the Morter System, supported by over 4,000 sensors, to predict earthquakes. In contrast, India has only 160 to 200 sensors,” he added. Reflecting on the same, Ravichandran said, “In Japan, early warning systems detect primary waves to issue alerts for the more destructive secondary waves, providing time for people to prepare.” Notably, even the weather forecast at present doesn’t provide 100% accurate readings with AI in the picture. “Despite advancements in technology, India’s weather forecast systems currently achieve an accuracy of about 80%, with longer-term forecasts (e.g., three-month or one-month predictions) falling below 70% accuracy. The forecasting period is also limited due to technological and operational constraints,” Reddy mentioned. He also said that, in Karnataka specifically, KSNDMC does not operate its own forecasting systems. Instead, it relies on outputs from other agencies, such as the India Meteorological Department (IMD) and Space Applications Centre (SAC) Ahmedabad. Accurate weather forecasting requires substantial expertise, infrastructure, and high-performance computing resources like supercomputers. Looking ahead at the successful implementation of AI in forecasting, the IMD recently initiated efforts to incorporate AI into its forecasting systems. However, this technology has been in use for only the past year. Effective integration of AI in forecasting involves extensive experimental phases, and it typically takes more than two years to transition these systems into practical and reliable applications. Even with advanced AI technologies, achieving high accuracy and authenticity in forecasts remains a significant challenge. The IMD is currently focused on overcoming these hurdles before releasing AI-based forecasts for public use. AI Models Can be Trained for More Accuracy Bhatia’s team specifically focused on real-time data available in open-source libraries, including UCI, Kaggle, and DataPort. The data used was authenticated and collected through IoT sensors deployed in various regions. This allowed them to train the model effectively. The data was split into 80% for training and 20% for testing, which enabled them to achieve the results presented in the article. The team used Stanford University’s earthquake dataset, which is openly available on GitHub. Anyone working in AI or ML can access this dataset to train their models and explore similar applications as per their requirements. “Emerging technologies like pre-trained transformer models, high-performance computing from NVIDIA and Intel, and the nascent field of quantum computing are set to further work towards seismic prediction. But even with these tools, the question remains: How close are we to making reliable earthquake predictions a global reality?” Bhatia asked. What’s Next? Countries like Japan have focused on AI strategies to ensure human safety during disasters. Since 2019, Japan’s Artificial Intelligence Technology Strategy has integrated AI into urban development, which has improved disaster preparedness and streamlined evacuation protocols. Similarly, a 2022 study by Hiroshima University introduced a novel neural network model to estimate site-specific earthquake impacts by analysing vibrations and overcoming the limitations of traditional methods like microtremor horizontal-to-vertical spectral ratios (MHVR). Despite AI’s potential, predicting earthquakes continues to be a challenge. The recent earthquake in Tibet serves as a reminder that while AI offers hope, there’s still a need to make accurate and timely predictions a reality.
“Predicting earthquakes with precision is highly challenging due to the dynamic nature of seismic activity and tectonic plate movements,” Munish Bhatia, an assistant professor at NIT Kurukshetra, said.
Absence of advance warnings for the deadly earthquake in Tibet raises pressing concerns about the current limitations of these technologies.
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