Earthquakes are highly destructive natural calamities that can have disastrous consequences for the environment, human lives, and property. 

There has been a growing fascination with forecasting earthquakes and thoroughly comprehending the mechanics that cause them. However, earthquakes remain the most unpredictable natural catastrophe. Satellite data, global positioning system (GPS), interferometric synthetic aperture radar (InSAR), and various types of seismometers, including microelectromechanical system (MEMS) seismometers, ocean bottom seismometers, and distributed acoustic sensing (DAS) systems, have proven highly effective in earthquake prediction. 

Although there have been improvements in the technology used to record, store, and analyze seismic waves, accurately predicting earthquakes' time, location, and magnitude still needs to be improved. However, recent advancements in AI and the Internet of Things (IoT) have demonstrated encouraging capabilities to provide further insights and forecasts.

Existing approaches

Recently, numerous studies have utilized machine learning algorithms' intricate prediction capabilities to examine intricate patterns in past seismic activity, meteorological data, acceleration, velocity data, and other factors to forecast earthquakes.

• The Chinese researchers presented a seismic forecasting technique that utilizes artificial immunity and the danger hypothesis. This earthquake indicator system combined different characteristics observed during seismic inactivity and activity.
• A consortium of academics from China, Singapore, the United States, the United Kingdom, and Italy has presented a revolutionary framework for earthquake prediction. The authors introduced the innovative Inverse Boosting Pruning Trees (IBPT) model, which utilizes satellite data consisting of ten factors: infrared sensing, hyperspectral imaging, and gas sensing signals.
• A team of researchers from Malaysia and Indonesia undertook a study to examine the precision of several artificial intelligence algorithms in forecasting earthquakes in Malaysia. 
• Researchers from the United Arab Emirates studied using artificial intelligence (AI) to forecast earthquakes in Terengganu, Malaysia. The study entailed examining meteorological data from various stations in Terengganu utilizing Machine Learning (ML) techniques.
• The Russian researchers evaluated the efficacy of using GPS data, mainly the time series of surface displacements, to predict earthquakes systematically. They focused on earthquakes with magnitudes greater than 6.0 in Japan (2016-2020) and above 5.5 in California (2013-2020).
• The Egyptian researchers tackled the problem of distinguishing between seismic occurrences and quarry blasts in the northeastern region of Egypt, where quarry booms taint the seismicity inventory.
• A group of researchers from Pakistan recently presented a groundbreaking study on earthquake prediction frameworks utilizing Federated Learning (FL). According to the authors, the suggested FL framework outperformed the previously developed ML-based earthquake prediction models regarding efficiency, dependability, and precision. Additionally, they indicated that their proposed validated framework was employed to evaluate multidimensional seismic data obtained from the western Himalayas.
• The Chinese and Pakistani researchers have developed a sophisticated Early Warning System (EWS) for forecasting earthquakes. The technique was utilized on datasets from Alaska and Japan, which are known for their high seismic activity, to forecast earthquake magnitudes. 
• The Moroccan researchers present a location-dependent earthquake prediction system utilizing recurrent neural network methods.
• Kazakhstan researchers have proposed an innovative approach to improve the accuracy of earthquake detection by optimizing each stage of the detection process. The hypothesis suggests that earthquake events demonstrate cyclical tendencies, and using only magnitude and depth predictors for historical data may enable the prediction of future devastating earthquakes.
• Researchers from India and Saudi Arabia have introduced a paper that suggests a cooperative framework for earthquake monitoring and prediction, utilizing IoT-Edge technology and combining cloud and edge computing. This model showcases the capacity for intelligent earthquake forecasting with increased precision and effectiveness.
• In 2023, Indian researchers employed deep-learning techniques to forecast earthquake magnitude. They utilized eight seismic indicators derived from earthquake databases in Japan, Indonesia, and the HinduKush Karakoram Himalayan (HKKH) region and were mathematically calculated.
• Egyptian researchers have developed a 2-s Machine Learning Earthquake Intensity Determination (2 S-ML-EIOS) model that can quickly determine the intensity of an earthquake within 2 seconds of the P-wave beginning. It is essential for early warning systems. When included in a centralized IoT system, their model can quickly transmit alerts and provide instructions to concerned authorities for rapid action, demonstrating its effectiveness for Earthquake Early Warning Systems (EEWS).  

Conclusion

The researchers examined the utilization of AI-driven Models and IoT-based technologies for forecasting earthquakes, the constraints of existing methods, and unresolved research concerns. They discuss the challenges in earthquake prediction caused by limited data, inconsistencies, the variety of earthquake precursor signals, and the earth's geophysical composition.

Furthermore, they analyze potential methodologies or remedies that scientists can utilize to tackle the difficulties they encounter in earthquake prediction. The analysis relies on the practical implementation of AI and the Internet of Things (IoT) in many domains.

Source: https://www.sciencedirect.com/science/article/pii/S2666544124000169?via%3Dihub#bib6

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