Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm

Mirrashid, Masoomeh

doi:10.1007/s11069-014-1264-7

Cited by 60 publications

(33 citation statements)

References 17 publications

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“…The Neuro fuzzy expert system has been used in many articles to analyze multiple aspects of data for earthquake predictions. Reference [28] combined grid partition, subtractive clustering and fuzzy C-means (FCM) for the development of models using NFES structure. Reference [5] applied NFES to compute land sliding susceptibility using statistical index (WI).…”

Section: Neuro Fuzzy Expert System (Nfes)mentioning

confidence: 99%

“…These tools and techniques have been summarized in Table 9. Annealing, Sparsespike 1.8 [25] Classification and regression trees(CART) 1.8 [49] Fuzzy C-mean 4 [28,77] Upgraded IF THEN ELSE 4 [27,83] Normalized fuzzy peak ground acceleration (FPGA) 1.8 [8] Aeronautical reconnaissance coverage Geographic information system (ARC/INFO GIS) 1.8 [84] Geographic information system (GIS), Multi criteria decision analysis (MCDA) 4 [15,82] Multilayer Preceptron -Rule Based (MLP-RB) 1.8 [21] Nearest neighbor Invariant Riemannian metric (AIRM) 1.8 [52] WI (Weighted index) 1.8 [5] Knowledge extraction based on evolutionary learning (KEEL) 1.8 [10] Particle SWARM Optimization (PSO) 1.8 [56] Apache SPARK 1.8 [59] Kernal Fisher Discriminant Algoritthm (KFDA) 1.8 [60] Novel earthquake early warning system (NEEWS) 1.8 [64] Accuracy of results obtained through the proposed expert system for making earthquake predictions using a training set (TS) or independent test set (ITS) has been listed in Table 10.…”

Section: Basic Analysismentioning

confidence: 99%

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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: Neuro Fuzzy Expert System (Nfes)mentioning

confidence: 99%

Section: Basic Analysismentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…Today, soft computing (SC) has many applications in engineering problems [7][8][9][10]. There are numerous articles on the use of SC in civil engineering such as earthquakes [11,12] dams [13], concrete [14] and structural control [15]. Also, these methods are considered to estimate the capacity of structural elements [16,17] instead of finite element analysis which is a time-consuming approach [18,19].…”

Section: Introductionmentioning

confidence: 99%

Moment capacity estimation of spirally reinforced concrete columns using ANFIS

Naderpour

Mirrashid

2019

Complex Intell. Syst.

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This paper presents a predictive model based on adaptive neuro-fuzzy inference system namely ANFIS to determine the moment capacity of spiral-reinforced concrete columns. For this purpose, five input parameters including the longitudinal reinforcement index, transverse reinforcement index, axial force, diameter to length ratio and also shear force were considered to estimate the moment capacity. A collection of experimental database was applied to train and test the proposed system. This database includes 82 spiral-reinforced concrete columns (with flexure failure) which were reported in the literature and modified by PEER as a uniform database of cantilever columns. The model is created by fuzzy C-means algorithm with four cluster and Gaussian membership functions, also trained and tested by 70 and 12 datasets, respectively. It was concluded that the model of this study with high accuracy could be able to estimate the moment capacity.

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“…The predictive equations are developed for peak ground acceleration and response spectra for soil and rock sites and compared to the available groundmotion data [7]. Mirrashid [22] investigated the prediction of future earthquakes that would occur with a magnitude of 5.5 Richter scale or greater using an adaptive neuro-fuzzy inference system (ANFIS). The results showed that ANFIS had higher accuracy to predict earthquake magnitude [22].…”

Section: Introductionmentioning

confidence: 99%

“…Mirrashid [22] investigated the prediction of future earthquakes that would occur with a magnitude of 5.5 Richter scale or greater using an adaptive neuro-fuzzy inference system (ANFIS). The results showed that ANFIS had higher accuracy to predict earthquake magnitude [22]. Giard in i et al [8] focused on the summary of the ordered network structure of earthquakes greater than 8.0, supplements new information of three earthquakes greater than 8.0 occurred in Nepal (1833, 1934, and 2015).…”

Section: Introductionmentioning

confidence: 99%

Statistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model

Sivandi‐Pour

Farsangi

2019

IJE

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A B S T R A C TT he Iranian plateau has been known as one of the most seismically active regions of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. Earthquakes are regularly felt on all sides of the region. Prediction of the occurrence location of the future earthquakes along with determining the probability percentage can be very useful in decreasing the seismic risks. Determining predicted locations causes increasing attention to design, seismic rehabilitation and evaluating the reliability of the present structures in these locations. No exact method has been approved for predicting future earthquake parameters yet. In recent years, more attention is paid to the earthquake magnitude prediction, but no study has been done in the field of probable earthquake occurrence hazard zonation. In this study, locations of future earthquakes in Iran were predicted by self-organized artificial neural networks (ANN). Then probable seismic risk zoning map was drawn by the statistical analyses, and the results indicated that the maps can properly predict future seismic events.

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Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm

Cited by 60 publications

References 17 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Moment capacity estimation of spirally reinforced concrete columns using ANFIS

Statistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model

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