Magnitude of Earthquake Prediction Using Neural Network

Suratgar, Amir Abolfazl; Setoudeh, Farbod; Salemi, A.H.; Negarestani, Ali

doi:10.1109/icnc.2008.781

Cited by 16 publications

(10 citation statements)

References 4 publications

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“…Where y 1 and y 2 is the prediction result of SVM and NN, γ was the final result, and α was set to 0.5. Suratgar et al [96] proposed a NN-based solution for prediction of earthquake magnitude. They used data of Iran from the year 1970 to the year 1976 for simulation purposes.…”

Section: Cheraghi and Ghanbarimentioning

confidence: 99%

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

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Section: Cheraghi and Ghanbarimentioning

confidence: 99%

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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“…Radial Basis Function is considered to provide faster prediction and better results than Back Propagation (Ying et al 2009). Based on Tehran Geophysics Research Center, Suratgar et al (2008) predicted earthquake magnitude. The parameters taken into account included geomagnetic field declination, horizontal component and hourly relative humidity, rain rate per day, and ground temperature.…”

Section: Related Workmentioning

confidence: 99%

BAT-ANN based earthquake prediction for Pakistan region

2016

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Earthquakes are natural disasters which may result in heavy losses. Accurate prediction of the time and intensity of future earthquakes can lead to minimizing losses due to earthquakes. A number of earthquake predictions have been proposed based on mathematical and statistical models. In this paper, we present an earthquake prediction technique using Bat Algorithm (BA) and Feed Forward Neural Network (FFNN). The BA is used to train the weights of the FFNN to predict future earthquakes on the basis of past input data. Experimental results show that our proposed approach is highly comparable and more stable than Back Propagation Neural Network (BPNN) with respect to accuracy.

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“…The recurrent neural network was constructed for predicting the magnetic storm intensity [ 20 ]. Forecasting the earthquake magnitudes based on the neural networks were considered in [ 21 , 22 ]. A new signal processing algorithm (WANEH) inspired by the deep learning paradigm was presented in [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

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Solovieva

et al. 2018

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The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

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Magnitude of Earthquake Prediction Using Neural Network

Cited by 16 publications

References 4 publications

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

BAT-ANN based earthquake prediction for Pakistan region

The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

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