Analysis of the Impact of Removal of Aftershocks from Catalogs on the Effectiveness of Systematic Earthquake Prediction

Гитис, В. Г.; Derendyaev, A. B.; Petrov, K. N.

doi:10.1134/s106422692006011x

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Testing of the application of the method of the minimum area of alarm was carried out according to the fields F 8 , F 9 , and F 10 , which were not used in our previous works (Gitis and Derendyaev, 2019;Gitis et al, 2020a). These fields turned out to be more informative than F 1 − F 7 fields.…”

Section: Discussionmentioning

confidence: 99%

“…Earthquake catalogs were not cleared of aftershocks. Earthquake prediction by the method of the minimum area of alarm based on complete earthquake catalogs and catalogs cleared of aftershocks is analyzed in Gitis et al (2020a). The formal rule defines an unambiguous choice of the area of analysis, which allows us to compare the forecast results obtained by different methods and for different fields of features.…”

Section: Methodsmentioning

confidence: 99%

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The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

Гитис

Derendyaev

2020

Front. Earth Sci.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

Гитис

Derendyaev

2020

Front. Earth Sci.

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“…In the ancient day, earthquakes were attributed to supernatural power and interpreted as punishment by God for our sinful society [1,2]. Aristotle (384-322 B.C.)…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of earthquake prediction using machine learning classifiers, curve fitting, and neural modeling

Chittora

Chakrabarti

Debnath³

et al. 2022

Preprint

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An earthquake is one of the most massive natural disasters which happens unexpectedly shaking the earth's surface. Due to earthquakes, not only infrastructure but also buildings get damaged thereby affecting lifestyle. For the early-stage prediction of the earthquake impact, machine learning can play a vital role, and this entails the novelty of the work. For this perception, six different machine learning classifiers namely Artificial Neural Network, Random Tree, CHAID, Discriminant, XGBoost Tree, and Tree-AS on six datasets of different regions of India. All the algorithms have been applied to each dataset. The objective of the research is to predict the value of magnitude for the future earthquake in India and nearby regions from the historical data on earthquakes. From the result, It has been observed that for Andaman & Nikobar dataset XGBoost Tree achieved the highest accuracy with 99.10%, for the Gujarat dataset Tree-AS achieved the highest accuracy with 91.67%, for the North India dataset Artificial Neural Network achieved the highest accuracy with 99.13%, for North East India dataset XGBoost Tree achieved the highest accuracy with 99.04%, for Nepal-UP-Bihar dataset XGBoost Tree achieved the highest accuracy with 99.01%, for Nearby India’s Country dataset XGBoost Tree achieved the highest accuracy with 92.12%. From all the results, it has been noted that XGBoost tree classifier performed well in most datasets., the Curve has been made between magnitude & gap, magnitude & magnitude error, and magnitude and depth error for finding the mathematical relation between them.

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“…In the early ages, earthquakes were believed to have occurred due to certain supernatural forces [1,2]. It was none other than Aristotle (384-322 B.C.)…”

Section: Introductionmentioning

confidence: 99%

Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

Debnath¹,

Chittora²,

Chakrabarti

et al. 2021

Sustainability

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Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

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Analysis of the Impact of Removal of Aftershocks from Catalogs on the Effectiveness of Systematic Earthquake Prediction

Cited by 5 publications

References 9 publications

The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

Experimental analysis of earthquake prediction using machine learning classifiers, curve fitting, and neural modeling

Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

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