Correlating Thermal Anomaly with Earthquake Occurrences Using Remote Sensing

Mukhopadhyay, Utpal; Sharma, Richa; Anwar, Shamama; Dutta, Atma Deep

doi:10.1007/978-3-030-14118-9_85

“…Reference [35] applied different machine learning algorithms namely support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and Naïve Bayes (NB) algorithms in R programming language for earthquake prediction using seismic dataset of India. Reference [36] studied the thermal anomalies that happened before the earthquake occurred in Imphal, India, in 2016 and investigated multiple seismic facts through satellite data using machine learning algorithms for an earthquake. Reference [37] collected records of aftershocks of the Kermanshah (Iran) Earthquake and applied different machine learning (ML) algorithms, including Naive Bayes, k-nearest neighbors, a support vector machine, and random forests to predict future earthquakes by observing aftershock patterns.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Tehseen

¹

,

Farooq

²

,

Abid

³

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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“…A three-layer artificial neural network with Levenberg-Marquardt learning was introduced by [36] to represent the relationship between earthquake and radon. Using a machine learning method, thermal anomalies were observed before the occurrence of the earthquake in Imphal, India, in 2016, and analyzing different seismic certainty through satellite data for an earthquake was studied by [37]. [38] using R programming language applying different machine learning methods like SVM, KNN, RF, and NB algorithms for earthquake prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Chittora

¹

,

Chakrabarti

²

,

Debnath³

et al. 2022

Preprint

1

0

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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.

show abstract

“…A three-layer arti cial neural network with Levenberg-Marquardt learning was introduced by [36] to represent the relationship between earthquake and radon. Using a machine learning method, thermal anomalies were observed before the occurrence of the earthquake in Imphal, India, in 2016, and analyzing different seismic certainty through satellite data for an earthquake was studied by [37]. [38] using R programming language applying different machine learning methods like SVM, KNN, RF, and NB algorithms for earthquake prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Chittora

¹

,

Chakrabarti

²

,

Debnath³

et al. 2022

Preprint

5

0

View full text Add to dashboard Cite

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.

show abstract

Correlating Thermal Anomaly with Earthquake Occurrences Using Remote Sensing

Cited by 3 publications

References 8 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

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

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

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