Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management.

The National Center for High-Performance Computing (NCHC) and Academia Sinica have developed an artificial intelligence (AI) model that could help researchers predict earthquakes one day in advance. The model could predict earthquakes based on precursors to tectonic activity, researchers said. The research team, led by Academia Sinica researcher Lee Lou-chuang (李羅權) and NCHC associate researcher Tsai Tsung-che (蔡宗哲), developed an AI model using total electron content (TEC) data and the Taiwania 2 supercomputer. The model could predict a magnitude 6 or higher earthquake one day in advance by analyzing data from the previous 30 days, they said. Past studies also found that atmospheric TEC within a 50km radius of the epicenter of an earthquake show signs of change prior to a large earthquake, the Central Weather Bureau's Seismological Center said, adding that TEC above Taiwan proper was low just before the 1999 Jiji earthquake.

A group of researchers unveiled a new method for using artificial intelligence (AI) to enhance our ability to read seismic waves and, in doing so, improve our understanding of how they begin, and even how they come to a stop. Published in Nature Communications, the paper details a method that automates earthquake detection at the same time as tuning out much of the noise inherent to seismic data. Mostafa Mousavi and a team of researchers use artificial intelligence to focus on millions of tiny subtle shifts in the Earth's crust. They hope that these tiny movements might act as a Rosetta Stone of sorts for deciphering warning signs for big earthquakes. "By improving our ability to detect and locate these very small earthquakes, we can get a clearer view of how earthquakes interact or spread out along the fault, how they get started, even how they stop," Stanford geophysicist Gregory Beroza, one of the paper's authors, explained in a Stanford University press release.

Data Science Fails – If It Looks Too Good To Be True… You've probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. AI can diagnose covid19 in seconds from a chest scan. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true.

In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. The artificial neural network (ANN) (shallow or deep) is rapidly rising as one of the most powerful go-to techniques not only in data science [LeCun et al., 2015; Jordan and Mitchell, 2016] but also for solving hard and intractable problems of Physics (e.g., many-body problem [Carleo and Troyer, 2017], chaotic systems [Pathak et al., 2018], high-dimensional partial differential equations [Han et al., 2018]). This is justified by the superior performance of ANNs in discovering complex patterns in very large datasets with the advantage of not requiring feature extraction or engineering, as data can be used directly to train the network with potentially great results. It comes as no surprise that machine learning at large -- including ANNs -- has become popular in Statistical Seismology [Kong et al., 2019] and gives fresh hope for earthquake prediction [Rouet-Leduc et al., 2017; DeVries et al., 2018].

Artificial intelligence and machine learning have already established their stronghold in areas like software, industries, healthcare and even the financial sectors. AI's ability to learn and predict has made it immensely successful in the above mentioned areas. Now, scientists and technology majors are using AI to try and save millions of lives that are affected due to natural calamities like floods, storms, earthquakes and forest fires every year all over the world. Making use of the data that is already available, AI will now be able to predict storms and other natural disasters thus preventing loss of lives. After the floods wreaked havoc and lead to the death of over 300 people in Kerala recently, several established IT companies are joining hands and announcing innovations to prevent such incidents in the future.

Scientists have developed an artificial intelligence (AI) system to successfully predict earthquakes, an advance that may help prepare for natural disasters and potentially save lives. The study, published in the journal Geophysical Review Letters, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Researchers from University of Cambridge in the UK and Boston University in the US studied the interactions among earthquakes, precursor quakes and faults, with the hope of developing a method to predict earthquakes. Using a lab-based system that mimics real earthquakes, they used machine learning techniques to analyse the acoustic signals coming from the'fault' as it moved and search for patterns. Researchers used steel blocks to closely mimic the physical forces at work in a real earthquake, and also records the seismic signals and sounds that are emitted.

A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.

A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.

Earthquakes are, by their nature, unpredictable. Although geologists understand why and how the tremors occur, forecasting them more than a few minutes ahead is very difficult. A team of scientists believes that machine learning could help solve this problem one day. A paper published Wednesday in the Geophysical Research Letters describes a method that relies on listening for acoustic signals from a laboratory simulation of failing fault lines. Stress is applied to two heavy steel blocks, causing them to slip and slide over one another like tectonic plates during an earthquake.