Notice of Retraction: Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier

Dehbozorgi, Leila; Farokhi, Fardad

doi:10.1109/iita-grs.2010.5602504

Cited by 12 publications

(11 citation statements)

References 12 publications

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“…In this study, we propose a hybrid approach for earthquake predicting using Bat Algorithm and Artificial Neural Network. The diversity of Bat Algorithm is combined with adaptability and efficient modeling of Neural Network to gain more accurate prediction results than many other proposed techniques (Dehbozorgi and Farokhi 2010;Alarifi et al 2012). We have experimentally shown that proposed BAT-ANN is highly comparable with respect to BPNN with respect to prediction accuracy.…”

Section: Related Workmentioning

confidence: 85%

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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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Section: Related Workmentioning

confidence: 85%

“…For testing features, signals were converted into vectors and inserted into neuro-fuzzy classifier. Feature selection is performed by UTA algorithm, after training and testing is applied (Dehbozorgi and Farokhi 2010). Shah et al utilized dataset of Southern California Earthquake Data Center (SCEDC).…”

Section: Related Workmentioning

confidence: 99%

BAT-ANN based earthquake prediction for Pakistan region

2016

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“…Dehbozorgi and Farokhi [51] proposed a neuro-fuzzy classifier for predicting short-term earthquakes using seismogram data five minutes before the earthquake. The equal number of seismogram signals were selected, which have and do not have an earthquake after five minutes.…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

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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“…Literatürde; Nöro-Bulanık sınıflandırıcı bir uygulama ile kaydedilen sismogramların verileri kullanarak kısa-süreli deprem tahmininde doğruluk %82 ile beş dakika önce 978-1-4799-4874-1/14/$31.00 ©2014 IEEE depremler tahmin edebilir. Yardımcı olarak istatistiksel, entropi, Ayrık Dalgacık Dönüşümü, Hızlı Fourier Dönüşümü, Chaotic Özellikleri (Maksimum Lyapunov Üssü), Güç Spektral Yoğunluğu kullanılmıştır [5]. DEMETER uydusu ve geri beslemeli Yapay Sinir Ağı kullanılmıştır.…”

Section: Introductionunclassified

Earthquake prediction using seismic bumps with Artificial Neural Networks and Support Vector Machines

Çelik

Atalay

Bayer

2014

2014 22nd Signal Processing and Communications Applications Conference (SIU)

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ÖZETÇEYer kabuğunda oluşan kırılmalar sonucu aniden ortaya çıkan titreşimlerin dalgalar halinde yayılarak yer yüzeyini sarsmaya devam etmesiyle depremler oluşur. Deprem oluşumu, bu dalgaların yer kabuğunda yayılım biçimine, bunların ölçülmesine ve ölçüm yöntemlerine, ölçümlerden elde edilen kayıtların değerlendirilmesi gibi birçok değişkene bağlıdır. Olası depremlerin tahmin edilmesi ve zararlarının en aza indirilmesi önemli unsurlardan birisidir. Burada sadece sismik darbe verileri kullanılarak depremin önceden tahmin edilmesi için karar sistemleri oluşturulabilir. Bu noktada sismik darbe verileri önce sınıflandırılacak sonra test aşamasında karşılaştırmalı sonuçları irdelenecektir. Çalışmamızda maden ocağından elde edilen hazır sismik darbe verileri kullanılarak sınıflandırma algoritmaları ile sınıflandırmaya tabi tutulur. Sınıflandırmada Yapay Sinir Ağları ve Destek Vektör Makineleri kullanılmıştır. Yapay Sinir Ağları ile sınıflandırma sonucunda %83 oranında erken tespit saptanmıştır. Destek Vektör Makineleri ile yapılan sınıflandırmada %91 oranında erken tespit saptanmıştır. ABSTRACT Earthquakes are what happens when immediate vibrations which shake earth surface, spread as waves as a result of earth crust cracks. Earthquakes depend on variables such as the way of spreading of these waves, calculation of these waves and calculating methods, evaluations of these recorded data sets. Predicting probable earthquakes and minimizing the damages are the important factors. Decision systems can be developed only through using seismic bump data. At this point, seismic data will be classified first and then comparative results will be analyzed at the test stage. We use end seismic data obtained from mine pit which are classified through classification algorithms. Artificial Neural Networks and Support Vector Machine are used in the classification. Early detection rate is calculated as 83% with the classification through Artificial Neural Network. Early detection rate is calculated as 91% with the classification through Support Vector Machine.

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Notice of Retraction: Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier

Cited by 12 publications

References 12 publications

BAT-ANN based earthquake prediction for Pakistan region

BAT-ANN based earthquake prediction for Pakistan region

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

Earthquake prediction using seismic bumps with Artificial Neural Networks and Support Vector Machines

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