Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict
Volcanic eruption refers to a natural catastrophe on Earth that poses imminent danger to communities surrounding volcanoes. Therefore, ongoing monitoring of volcanic processes is crucial for effective analysis and observation of volcanic activities preceding an eruption. In response to this, the stu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-03-01
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| Series: | HighTech and Innovation Journal |
| Subjects: | |
| Online Access: | https://hightechjournal.org/index.php/HIJ/article/view/1115 |
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| Summary: | Volcanic eruption refers to a natural catastrophe on Earth that poses imminent danger to communities surrounding volcanoes. Therefore, ongoing monitoring of volcanic processes is crucial for effective analysis and observation of volcanic activities preceding an eruption. In response to this, the study presents a novel hybrid time series approach, integrated with machine learning techniques, to enhance the identification and classification of seismic events associated with volcanic eruptions. In this case, time series techniques, including STA/LTA, template matching, and autocorrelation, were implemented to facilitate the detection and classification process. The challenges, however, lie in addressing noise and ensuring accuracy in the analysis of seismic signals. To resolve this, a new hybrid time series method was proposed to improve signal analysis accuracy by integrating multiple time series techniques. In practice, the dataset was collected from Mount Merapi in Indonesia between 2019 and 2021, consisting of a compilation of seismic data categorized by event type, thus enhancing classification accuracy. On top of that, prior to implementing machine learning techniques for signal classification, the hybrid method was employed to efficiently remove noise, ensuring that genuine seismic events were clearly distinguished from spurious signals. Notably, the experimental learning rate was set at 0.01. The results demonstrated that the proposed hybrid method outperformed stand-alone time series techniques, achieving an accuracy of 0.93 to 0.95. This signifies the effectiveness of precise seismic event recognition and categorization, greatly enhancing the volcano monitoring system. Furthermore, the findings offer substantial improvements in the forecasting and risk mitigation associated with volcanic eruptions, hence, advancing reliable seismic analysis methodologies. Ultimately, the method enhances hybrid methods and machine learning for seismic event analysis and volcano monitoring.
Doi: 10.28991/HIJ-2025-06-01-08
Full Text: PDF |
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| ISSN: | 2723-9535 |