The Application of Machine Learning in Determining Earthquake Magnitude as an Early Warning

Sheng, Xinxu; Li, Shanyou; Lu, Jianqi

doi:10.1088/1755-1315/783/1/012089

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Before accepting a client's project, it's critical to appraise, assess, or estimate it. So, there are several research papers on this topic that use estimation or machine learning techniques [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Ikram¹,

Jalil²,

Ngah³

et al. 2023

Computers, Materials &Amp; Continua

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Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients' projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in developing countries while providing services to developed countries. In the current study, Extreme Learning Machine's (ELM's) variant called Deep Extreme Learning Machines (DELMs) is used. A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model. The proposed DELM's based model evaluations achieved 90.017% training accuracy having a value with 1.412 × 10 -3 Root Mean Square Error (RMSE) and 85.772% testing accuracy with 1.569 × 10 −3 RMSE with five DELMs hidden layers. The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies. The current study also concludes DELMs as the most applicable and useful technique for OSMO client's project assessment.

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Section: Introductionmentioning

confidence: 99%

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Ikram¹,

Jalil²,

Ngah³

et al. 2023

Computers, Materials &Amp; Continua

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“…In the research on earthquake estimation, a machine-learning method for estimating magnitude through various parameters is conducted (Sheng, Li, and Lu, 2021;Beroza, Segou, and Mostafa-Mousavi, 2021;Mousavi and Beroza, 2020;Schäfer and Wenzel, 2019;Ochoa, Niño, and Vargas, 2018). An end-to-end method for earthquake detection, including phase detection, association, and location tasks, has also been carried out using deep learning method (Zhu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Ensemble Deep Learning NARX System for Estimating the Earthquake Occurrences in the Subduction Zone of Java Island

Nugroho,

Subiantoro,

Kusumoputro

2023

IJTech

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Earthquake is a natural hazard causing significant damage and loss of lives. In recent years, there has been a growing interest in the development of earthquake early warning system, using machine learning methods. One of the promising method is the use of Neural Network-Based Nonlinear AutoRegressive with eXogenous inputs (NN-based NARX) system, which has gained attention for the potential to improve the prediction accuracy and the robustness of earthquake early warning system. NN-based NARX system is composed of an effective recurrent neural network in modeling the time-series data. Therefore, this research aimed to investigate the performance of Ensemble Deep Learning NARX system, regarding earthquake occurrences estimation in the subduction zone, including Sunda Strait, Southern Java, and Bali Region. Ensemble Deep Learning NARX system was developed as the predictor to improve the performance characteristics of NNbased NARX system in determining earthquake occurrences in the subduction zone of Java Island. The proposed Ensemble model combined multiple NARX system, each trained on a different subset of earthquake data, using the diversity and complementarity of individual model. The results showed that Ensemble Deep Learning NARX system outperforms individual model and traditional method, yielding a significantly improved estimation performance. The mean square error (MSE) of the testing data set was 5.97x10 -23 , 8.97x10 -24 , 9.73x10 -26 for Sunda Strait, Southern Java, and Bali Region, respectively. The research provided valuable insights for seismic hazard assessment, facilitating the development of proactive measures for earthquake mitigation and preparedness in the regions.

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The Application of Machine Learning in Determining Earthquake Magnitude as an Early Warning

Cited by 2 publications

References 2 publications

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Performance Analysis of Ensemble Deep Learning NARX System for Estimating the Earthquake Occurrences in the Subduction Zone of Java Island

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