Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks

Asencio-Cortés, Gualberto; Martínez‐Álvarez, Francisco; Troncoso, Alicia; Morales‐Esteban, Antonio

doi:10.1007/s00521-015-2121-7

Cited by 67 publications

(44 citation statements)

References 30 publications

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“…Reference [66] examined the efficiency of bio-inspired algorithms for supervised classification of on real datasets to handle emergencies. Reference [67] discussed the emerging Regional Earthquake likelihood models in making earthquake predictions with improved accuracy. Reference [68] used tree-based ensemble methodologies for earthquake prediction within time period of 15 days and calculated seismic features of Hindukush region applying machine learning methods for macro earthquake prediction.…”

Section: Discussionmentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

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Section: Discussionmentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…In literature, there are very limited studies available that specifically compares the performance of different neural networks on the basis of different set of inputs and the number of hidden layers (Lakshmi and Tiwari, 2006;Reyes et al, 2013;Asencio-Cortés et al, 2017;Perol et al, 2017). This study therefore is an attempt to address that gap to an extent.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Neural Networks (ANN) are increasingly used in predicting and classifying tasks because of their ability to capture the inherent complex relationship of a process with the set of inputs (Lakshmi and Tiwari, 2006;Madahizadeh and Allamehzadeh, 2009;Alarifi et al, 2012;Niksarlioglu and Kulahci, 2013;Reyes et al, 2013;Sriram et al, 2013;Zamani and Sorbi, 2013;Amar et al, 2014;Florido et al, 2016;Kurach and Pawlowski, 2016;Narayanakumar and Raja, 2016;Asencio-Cortés, et al 2017;Perol et al, 2017). The ANN modeling requires finding two important factors: set of inputs and set of hyper-parameters.…”

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confidence: 99%

Earthquake Forecasting Using Artificial Neural Networks

Bhatia

Pasari

Mehta

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences. * Corresponding author network (Lakshmi and Tiwari, 2006). For this particular task, there are so many factors involved in the process that other model based approaches cannot accommodate as accurately as neural network does (Perol et al., 2017).This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-823-2018 | © Authors 2018. CC BY 4.0 License.

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“…Zone Comparison [3] Azores (Portugal) [30] South California (USA) LM-BP, RBF [31] South California (USA) LM-BP, RBF [23] Sichuan (China) [39] Yunnan (China) BPNN [35] Northeast India BPNN [34] North California (USA) BPNN [27] Greece [2] Northern Red Sea Statistical predictors [33] Chile SVM, NB [25] Iberian Peninsula M5P, NB, SVM [24] Iberian Peninsula, Chile SVM, NB [42] Qeshm (Iran) [4] Alaska (USA) BPNN [45] Southwest Chine BPNN [7] Tokyo (Japan) KNN, SVM, NB, J48 [41] Tabriz (Iran) [6] Chile [22] China BPNN, PSO-BPNN [8] Hindukush (Pakistan) Random Forest, LPBoost ensemble Table 1: Summary of zones studied and algorithms used for comparative purposes…”

Section: Earthquake Prediction By Means Of Annmentioning

confidence: 99%

“…Moreover, there are other groups such as the Collaboratory for the Study of Earthquake Predictability (CSEP) [6] and that of [7] in New Zealand.…”

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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Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks

Cited by 67 publications

References 30 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Forecasting Using Artificial Neural Networks

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