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How machine learning can predict rare disasters – such as earthquakes or pandemics

How machine learning can predict rare disasters – such as earthquakes or pandemics


A team of researchers has developed a new framework that uses advanced machine learning and statistical algorithms to predict rare events without the need for large data sets.

Scientists can use a combination of advanced machine learning and sequential sampling techniques to predict extreme events without the need for large data sets, according to researchers from Brown and MIT.

When it comes to predicting disasters caused by extreme events (think earthquakes, epidemics, or “rogue waves” that can destroy coastal structures), computational modeling faces an almost insurmountable challenge: statistically speaking, such events are far too rare to There is enough data on them to use predictive models to accurately predict when they will happen next.

However, a group of scientists from Brown University and the Massachusetts Institute of Technology points out that this does not have to be the case.

In a study published in Nature Computational Science, researchers explain how they used statistical algorithms that require less data for accurate predictions, along with powerful machine learning technology developed at Brown University. This combination allowed them to predict scenarios, probabilities, and even timelines for rare events despite a lack of historical data.

In doing so, the research team found that this new framework could provide a way to get around the need for the huge amounts of data traditionally needed for these types of computations, and instead reduce the significant challenge of predicting rare events to a question of quality. on the quantity.

“You have to realize that these are random events,” said George Karniadakis, professor of applied mathematics and engineering at Brown University and author of the study. “An outbreak of a pandemic like COVID-19, an environmental disaster in the Gulf of Mexico, an earthquake, a huge wildfire in California, a 30-meter wave capsizing a ship — these are rare events and because they are rare, we don’t have much historical data. We don’t have enough samples from the past to predict the future. The question we address in the paper is: what is the best possible data that we can use to reduce the number of data points we need? “

The researchers found the answer in a sequential sampling technique called active learning. These types of statistical algorithms are not only able to analyze the data entered into them, but more importantly, they can learn from the information to label new, relevant data points that are equal or even more important to the outcome being computed. At the most basic level, they allow more to be done with less.

This is critical to the machine learning model the researchers used in the study. This model is called DeepOnet, and it is a type of artificial neural network, which uses interconnected nodes in successive layers that roughly mimic the connections made by neurons in the human brain. DeepOnet is known as the deep neural engine. It is more advanced and powerful than typical artificial neural networks because it is actually two neural networks in one, processing data in two parallel networks. This allows it to analyze huge sets of data and scenarios at breakneck speed to pull out equally huge sets of possibilities once it learns what to look for.

The bottleneck with this powerful tool, especially for rare events, is that deep nerve operators need to train tons of data to make efficient and accurate calculations.

In the paper, the research team shows that combined with active learning techniques, a DeepOnet model can be trained on what parameters or precursors to look for that lead to the catastrophic event someone is analyzing, even when there aren’t many data points.

“The motivation is not to take all possible data and put it in the system, but to proactively search for events that will signify rare events,” Karniadakis said. “We may not have many examples of the real event, but we may have those precursors. Through mathematics, we identify them, which along with real events will help us train this data-hungry operator.”

In the paper, the researchers applied the approach to identify parameters and different ranges of probability for dangerous surges during a pandemic, to find and predict rogue waves, and to estimate when a ship would break in half due to stress. For example, with rogue waves — those that are more than twice the size of surrounding waves — researchers have found that they can detect and determine when rogue waves form by looking at potential wave conditions that interact nonlinearly over time, resulting in waves that sometimes Three times its original size.

The researchers found that their new method is superior to traditional modeling efforts, and they believe it offers a framework that can efficiently detect and predict all kinds of rare events.

In the paper, the research team outlines how scientists should design future experiments so they can reduce costs and increase prediction accuracy. Karniadakis, for example, is already working with environmental scientists to use a new method for predicting weather events, such as hurricanes.

Reference: “Detection and Prediction of Extreme Events through Active Learning in Neurofactors” by Ethan Pickering, Stephen Guth, George M Karniadakis and Themistocles B. 0

The study was led by Ethan Pickering and Themistoklis Sapsis of the Massachusetts Institute of Technology. DeepOnet was introduced in 2019 by Karniadakis and other Brown researchers. They are currently seeking a patent for the technology. The study was supported by funding from the Defense Advanced Research Projects Agency, the Air Force Research Laboratory, and the Office of Naval Research.




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