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

Alarifi, Abdulrahman; Al‐Arifi, Nassir; Al-Humidan, Saad

doi:10.1016/j.jksus.2011.05.002

Cited by 85 publications

(49 citation statements)

References 24 publications

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“…[5] proposed a method for the estimation of peak ground acceleration using ANN by taking inputs like magnitude, hypocentral distance and average shear wave velocity. [3] proposed a method using ANN to forecast earthquakes in northern Red Sea area. They presented different statistical methods and data fitting such as linear, quadratic and cubic regression.…”

Section: Proposed Methodsmentioning

confidence: 99%

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Dutta¹,

Mishra²,

Naskar³

2013

Open Geosciences

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Abstract:In the proposed study, non-linear behavioral patterns in the seismic regime for earthquakes in the Himalayan basin have been studied using a complete, verified EQ catalogue comprised of all major events and their aftershock sequences in the Himalayan basin for the past 110 years . The dataset has been analyzed to give better decision making criteria for impending earthquakes. A series of statistical tests based on multi-dimensional rigorous statistical studies, inter-event distance analyses, and statistical time analyses have been used to obtain correlation dimensions. The time intervals of earthquakes within a seismic regime have been used to train the neural network to analyze the nature of earthquake patterns in the different clusters. The results obtained from descriptive statistics show high correlation with previously conducted gravity studies and radon anomaly variation. A study of the time of recurrence of the numerical properties of the regime for 60 years from 1950 to 2010 for the Himalayan belt for analysis of significant EQ failure events has been done to find the best fit for an empirical data probability distribution. The distribution of waiting time of swarm events occurring in the Himalayan basin follows a power-law model, while independent events do not fit the power-law distribution. This suggests that probability of the occurrence of swarm events [M 6.0] with frequent shaking may be more frequent than that of the occurrence of independent events of magnitude [M >6.0] in the Himalayan belt. We propose a three-layer feed forward neural network model to identify factors, with the actual occurrence of the maximum earthquake level M as input and target vectors in Himalayan basin area. We infer through a series of statistical results and evaluations that probabilistic forecasting of earthquakes can be achieved by finding the meta-stable cluster zones of the Himalayan clusters for the spatio-temporal distribution of earthquakes in the area.

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Section: Proposed Methodsmentioning

confidence: 99%

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Dutta¹,

Mishra²,

Naskar³

2013

Open Geosciences

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“…The artificial neural networks (ANN) are one of the most accurate and widely used predicting models that have fruitful applications in many engineering problems [28,29]. The basics of ANN are presented in [28,29]; in addition, the application of ANN to predict the performance and damage detection of structures showed that the ANN is a good tool that can be used in this area [18,19]. Bigdeli and Kim [12] used ANN to predict the behavior of irregular buildingcontroller system under dynamic loads and found that the ANN can be used to identify the performance of building structures.…”

Section: Shock and Vibrationmentioning

confidence: 99%

“…The total number of DOFs (42) after application of the boundary conditions (i.e., rigid diaphragm, Guyan reduction of vertical DOFs, and rotational DOFs around -and -axes) equals 9. The input signal generated artificially through shaking table [29] generates an acceleration signal having a waveform in which parameters such as strong motion duration, ramp times, and number of strong motion peaks are variables. The input and output signals generation and measurements are presented in [3,[31][32][33].…”

Section: Response Theory and Experimental Setupmentioning

confidence: 99%

The Performance of Structure-Controller Coupled Systems Analysis Using Probabilistic Evaluation and Identification Model Approach

Kaloop

Bigdeli

2017

Shock and Vibration

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This study evaluates the performance of passively controlled steel frame building under dynamic loads using time series analysis. A novel application is utilized for the time and frequency domains evaluation to analyze the behavior of controlling systems. In addition, the autoregressive moving average (ARMA) neural networks are employed to identify the performance of the controller system. Three passive vibration control devices are utilized in this study, namely, tuned mass damper (TMD), tuned liquid damper (TLD), and tuned liquid column damper (TLCD). The results show that the TMD control system is a more reliable controller than TLD and TLCD systems in terms of vibration mitigation. The probabilistic evaluation and identification model showed that the probability analysis and ARMA neural network model are suitable to evaluate and predict the response of coupled buildingcontroller systems.

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“…Great eort was invested on the Parkeld prediction experiment [2]. The results obtained made the scientic community wonder if earthquakes could be predicted at all.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2016

CRORR

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Abstract. The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate entire cities in just a few seconds, causing numerous casualties and huge economic loss. Great eort has been directed towards being able to predict these natural disasters, and taking precautionary measures. However, simultaneously predicting when, where and the magnitude of the next earthquake, within a limited region and time, seems an almost impossible task. Techniques from the eld of data mining are providing new and important information to researchers. This article reviews the use of articial neural networks for earthquake prediction in response to the increasing amount of recently published works and presenting claims of being eective. Based on an analysis and discussion of recent results, data mining practitioners are encouraged to apply their own techniques in this emerging eld of research.

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

Cited by 85 publications

References 24 publications

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

The Performance of Structure-Controller Coupled Systems Analysis Using Probabilistic Evaluation and Identification Model Approach

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