Prediction of earthquake magnitude using adaptive neuro fuzzy inference system

Pandit, Amiya; Biswal, K. C.

doi:10.1007/s12145-019-00397-w

Cited by 22 publications

(11 citation statements)

References 16 publications

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“…For training and testing of data, 70% of the data is used for training data while 30% is used for testing [ 31 ]. The given training data of the risk prediction is shown in Fig.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

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“…For training and testing of data, 70% of the data is used for training data while 30% is used for testing [ 31 ]. The given training data of the risk prediction is shown in Fig.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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“…Spatial and temporal changes in the crustal deformation process associated with the earthquakes depend on several parameters; doing the sensitivity analysis on these parameters is very important. Three different variables, namely, lithosphere thickness, asthenosphere viscosity and dip angle of fault, are some of these parameters [29][30][31][32].…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Earthquake prediction modeling using dynamic changes (Case Study: Alborz Region)

2021

IJE

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This study aims to investigate the computational effect of Earth's viscosity on the Coulomb stress changes. Therefore, several large earthquakes in the Alborz region are selected and Coulomb stress changes are calculated in them, then the Coulomb stress temporal changes are shown by assuming the earth as an elastic layer on a viscous-elastic half-space. The spatial and temporal changes of the crustal deformation process associated with earthquakes depend on several parameters including the thickness of the lithosphere, viscosity of the asthenosphere, and dip angle of fault. The findings of this study are presented by determining the impact of modeling results on each of the input parameters through the sensitivity analysis of co-seismic and post-seismic deformation due to the dip-slip and strike-slip faulting. In addition to the useful results reported for the impact of parameters, the obtained results indicate the occurrence of numerous aftershocks in a region with increased Coulomb stress from 0.1 to 0.8 bar and the non-occurrence or low-occurrence of aftershocks in a region with reduced Coulomb stress. In addition to the predicted locations of aftershocks, it is also possible to determine the location of the next major earthquake using Coulomb stress change calculations.

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“…The model was trained for 50 epochs, and the squash factor was selected as 1.25 for the training process. Pandit and Biswal [56] proposed ANFIS with GP and SC for predicting the magnitude of an earthquake. Ground motions…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

“…Kamath and Kamat [55] achieved RMSE of 0.059 for training and 0.408 for testing in predicting the magnitude of an earthquake using the ANFIS model. Pandit and Biswal [56] used ANFIS with SC to achieve correlation factor (R 2 ) of 0.8699 for forecasting earthquakes.…”

Section: A Rule-based Approachesmentioning

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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Prediction of earthquake magnitude using adaptive neuro fuzzy inference system

Cited by 22 publications

References 16 publications

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Earthquake prediction modeling using dynamic changes (Case Study: Alborz Region)

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

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