Application of neural network and ANFIS model for earthquake occurrence in Iran

Ahmad, Zaini; Sorbi, Mohammad Reza; Safavi, Ali Akbar

doi:10.1007/s12145-013-0112-8

Cited by 57 publications

(31 citation statements)

References 36 publications

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“…They are used to recognize the pattern of earthquake occurrences. It can also be employed for predicting the nature of the future events and as well as mitigation of earthquake risk [11], [14]. The seven seismicity indicators are shown in Table 1.…”

Section: Proposed Neural Networkmentioning

confidence: 99%

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Shodiq

Kusuma

Rifqi

et al. 2018

JOIV : Int. J. Inform. Visualization

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Abstract-A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Six cluster was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter's scale magnitudes.

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Section: Proposed Neural Networkmentioning

confidence: 99%

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Shodiq

Kusuma

Rifqi

et al. 2018

JOIV : Int. J. Inform. Visualization

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“…Asencio-Cortés et al [42] systematically studied the value of seismicity indicators as input for an ANN. Five dierent analyses were conducted on the shape of the training and test sets, the calculation of the b-value and adjustment of the most frequently collected indicators.…”

Section: Earthquake Prediction By Means Of Annmentioning

confidence: 99%

“…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%

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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“…Probabilistic neural network, also a type of ANN, was used to forecast the earthquake in southern California and presented good prediction accuracies for earthquakes of magnitudes between 4.5 and 6.0 [12]. The radial basis function neural network yielded more accurate and effective prediction re-sults than adaptive neural fuzzy, a kind of artificially intelligent method, in South Iran [13]. The ANN's earthquake prediction models were used in many other regions such as Greece [14], the northern Red Sea area [15], Chile [16,17] and East Anatolian fault region [18].…”

Section: Introductionmentioning

confidence: 99%

“…Many kinds of ANN methods were compared to search for the rational method to build the earthquake model [5,12,13,19]. Seismicity indicators were analyzed to choose the rational input parameters [11,16] and to consider the monitoring data [18,14,20].…”

Section: Introductionmentioning

confidence: 99%

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

Sheng¹,

Mu²,

Zhang³

et al. 2015

TOCIEJ

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Abstract:It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.

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Application of neural network and ANFIS model for earthquake occurrence in Iran

Cited by 57 publications

References 36 publications

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Untitled

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

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