Earthquake probability prediction in Sumatra Island using Poisson Hidden Markov Model (HMM)

Marisa,; Sembiring, Ukur Arianto; Margaretha, Helena

doi:10.1063/1.5139176

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…In the application of the K-Means algorithm, the data that can be used is numerical data in the form of numbers. Categorical data can still be used but must first be coded to facilitate the calculation of distances or similarity of characteristics possessed by each object [6].…”

Section: K-meansmentioning

confidence: 99%

Web Application of Classification of Potential Earthquake Region in Central Sulawesi

Islami,

Rachman,

Matira

et al. 2023

Advances in Physics Research

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An earthquake is a shaking incident of the earth due to the movement or shift of rocks in the lithospheric part. This occurs suddenly due to the movement of tectonic plates. Central Sulawesi is an area of Sulawesi Island that has a high level of seismicity. This is because Sulawesi Island has many active local faults that can trigger earthquakes. Based on the historical records, there have been several earthquakes that have occurred in Central Sulawesi, and some of that is caused by tsunamis, in 1996 at Toli-toli and Palu and the latest one occurred in Palu, Sigi, Donggala, and Parigi on 28 September 2018. This research aims to group the potential earthquake area in Central Sulawesi using the K-Means method by applying the R packages. The results show that we obtained the 2 cluster data. The data characteristic in cluster 1 consists of data that has a low depth so that it is more likely to damage infrastructures but it has no potential for tsunamis. Moreover, the data characteristic in cluster 2 consists of data that has high-depth consequences it does not have any potential to damage the infrastructure, but it could potentially have a tsunami. Finally, the web application for potential grouping earthquake areas in Central Sulawesi using the K-Means algorithm is obtained in this research. This is essential to minimize the impacts of the damage due to earthquakes and tsunamis.

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Section: K-meansmentioning

confidence: 99%

Web Application of Classification of Potential Earthquake Region in Central Sulawesi

Islami,

Rachman,

Matira

et al. 2023

Advances in Physics Research

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“…Marisa et al [67] tried to predict the probability of the number of earthquakes in 15 days period in Sumatra. First, the earthquakes with a magnitude greater than 4.9 and depth less than 70 km were selected.…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

“…[60] SVD and SVM Time, East-West and North-South polarity [61] SVR mmt., awsp., mg., mean temperature [62] SVR and HNN GR law, ser., ff., trt., src. [63] PR-KNN Time interval between aftershocks [64] HWT with RF, NB, J48, REP Tree, and BP Seismic signal [66] RST and DT (C4.5) Location and climatic factors [67] PHMM Initial probability, transition probability [68] KMC b-value, date of occurrence, mg. [69] KMC and TBS Seismicity indicators, increments of b-value [71] HKMC with ANN Seven seismicity indicators [72] b-value, Bath's law, and OU's law parameters [70] HKMC lo., la. [73] Dobrovolsky-based Clustering SES [74] AHC Over Sampling Seismicity indicators [75] GA based Clustering Time, lo., la., tremor mg., dpt.…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

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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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“…In the mathematical modeling attempts to formulate earthquake prediction using various statistical and mathematical methods [13]. For instance, Marisa et al [14] applied a Poisson hidden Markov model (PHMM) to predict the probability of an earthquake in Sumatra island. Dehghani and Fadaee [15] proposed a bivariate lognormal distribution-based probabilistic prediction method.…”

Section: Introductionmentioning

confidence: 99%

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

Kavianpour¹,

Kavianpour²,

Jahani³

et al. 2021

Preprint

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Earthquakes, as natural phenomena, have continuously caused damage and loss of human life historically. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Nevertheless, due to the stochastic character of earthquakes and the challenge of achieving an efficient and dependable model for earthquake prediction, efforts have been insufficient thus far, and new methods are required to solve this problem. Aware of these issues, this paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long shortterm memory (BiLSTM) models, which can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region. This model takes advantage of LSTM and CNN with an attention mechanism to better focus on effective earthquake characteristics and produce more accurate predictions. Firstly, the zero-order hold technique is applied as pre-processing on earthquake data, making the model's input data more proper. Secondly, to effectively use spatial information and reduce dimensions of input data, the CNN is used to capture the spatial dependencies between earthquake data. Thirdly, the Bi-LSTM layer is employed to capture the temporal dependencies. Fourthly, the AM layer is introduced to highlight its important features to achieve better prediction performance. The results show that the proposed method has better performance and generalize ability than other prediction methods.

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Earthquake probability prediction in Sumatra Island using Poisson Hidden Markov Model (HMM)

Cited by 8 publications

References 6 publications

Web Application of Classification of Potential Earthquake Region in Central Sulawesi

Web Application of Classification of Potential Earthquake Region in Central Sulawesi

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

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

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