Pseudo-acoustic inversion method and its application

Wang, Shuying; Liu, Huaishan; Wang, Shuhua; Tong, Siyou; Wang, Ruiming

doi:10.1109/fskd.2010.5569460

Cited by 2 publications

(4 citation statements)

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“…The proposed model has been able to forecast the rock burst incident in the coal mine of China. Multiple algorithms have been combined for development of the hybrid prediction model [24,25]. Ionospheric disturbance has been examined in [26] and a fuzzy logic-based gradient descent method has been proposed to forecast the ionospheric change parameters.…”

Section: Fuzzy Expert System (Fes)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: Fuzzy Expert System (Fes)mentioning

confidence: 99%

Section: Basic Analysismentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…The proposed model has been able to forecast the rock burst incident in the coal mine of China. Multiple algorithms have been combined for development of the hybrid prediction model [23], [33]. Ratnam et al [15] examined ionosphere disturbance and have proposed a Fuzzy logic-based gradient descent method to forecast the ionosphere change parameters.…”

Section: A Fuzzy Expert System (Fes)mentioning

confidence: 99%

“…Neuro-Fuzzy model has been used in combination with conservation of energy in angular momentum [30], seismic risk assessment of building and bridges in [31]. Hybrid model has been used in [32], [33] to predict seismic subsidence for earthquake prediction. ES has been used to evaluate the ergonomics system of oil refineries in [34].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Expert System for Earthquake Prediction in Western Himalayan Range

Tehseen

Farooq

Abid

2020

ELEKTRON ELEKTROTECH

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Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reproduce the performance of a human expert in earthquake prediction using expert systems. This research aims to predict future earthquakes having a magnitude 5.5 or greater. Previous earthquake data from 2000 to 2019 have been collected for this purpose. Since the earthquake data for the specified region have been reported on different magnitude scales, suitable relationships were determined to obtain uniform data. The uniform data have been used to calculate seismicity indicators according to the guidelines provided by Gutenberg-Richter’s scale for quantitative determination of earthquake features. The relationships among these seismic indicators have been used by the human expert to set the rule base of Fuzzy expert system. These rules have been mathematically validated and tested on instrumentally recorded earthquake data. The results obtained from the proposed FES presented 47 % accuracy in predicting future earthquakes that may occur in the 100 km radial area from 34.708 ° N, 72.5478 ° E.

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Pseudo-acoustic inversion method and its application

Cited by 2 publications

References 0 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Fuzzy Expert System for Earthquake Prediction in Western Himalayan Range

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