Forecast of underwater earthquakes with a great degree of probability

Turmov, G.P.; Korochentsev, V.I.; Gorodetskaya, E.V.; Mironenko, A.M.; Kislitsin, D.V.; Starodubtsev, O.A.

doi:10.1109/ut.2000.852525

Cited by 2 publications

(1 citation statement)

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“…Recursive sample-entropy technique for earthquake forecasting, where the earth data based on VAN method was used for the modelling [14]. Models based on measurement of elastic and electromagnetic waves to predict earthquakes and tsunami was done [15]. Earthquake hazard assessment was done using EaHaAsTo tool for visualization [16].…”

Section: Introductionmentioning

confidence: 99%

Earthquake trend prediction using long short-term memory RNN

Bhandarkar

Vardaan

Satish

et al. 2019

IJECE

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<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>

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Section: Introductionmentioning

confidence: 99%

Earthquake trend prediction using long short-term memory RNN

Bhandarkar

Vardaan

Satish

et al. 2019

IJECE

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Earthquakes magnitude predication using artificial neural network in northern Red Sea area

Alarifi

Al‐Arifi

Al-Humidan

2012

Journal of King Saud University - Science

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Since early ages, people tried to predicate earthquakes using simple observations such as strange or atypical animal behavior. In this paper, we study data collected from past earthquakes to give better forecasting for coming earthquakes. We propose the application of artificial intelligent predication system based on artificial neural network which can be used to predicate the magnitude of future earthquakes in northern Red Sea area including the Sinai Peninsula, the Gulf of Aqaba, and the Gulf of Suez. We present performance evaluation for different configurations and neural network structures that show prediction accuracy compared to other methods. The proposed scheme is built based on feed forward neural network model with multi-hidden layers. The model consists of four phases: data acquisition, pre-processing, feature extraction and neural network training and testing. In this study the neural network model provides higher forecast accuracy than other proposed methods. Neural network model is at least 32% better than other methods. This is due to that neural network is capable to capture non-linear relationship than statistical methods and other proposed methods.

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Forecast of underwater earthquakes with a great degree of probability

Cited by 2 publications

References 1 publication

Earthquake trend prediction using long short-term memory RNN

Earthquake trend prediction using long short-term memory RNN

Earthquakes magnitude predication using artificial neural network in northern Red Sea area

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