Review: Enhancing Volcanology Prediction Capabilities Through Machine Learning and Data Analysis

EasyChair Preprint 10432

Abstract

Predicting earthquakes and volcanic eruptions accurately and reliably remains a complex scientific challenge due to the dynamic nature and inherent uncertainties associated with these natural phenomena. While AI techniques have been explored and applied in the fields of seismology and volcanology, there is currently no method or AI model that can precisely forecast the exact timing, magnitude, or location of these events. However, AI can play a supportive role by assisting scientists and researchers in analyzing large volumes of seismic, geodetic, and other relevant data. Machine learning algorithms can be employed to identify patterns, correlations, and anomalies that may contribute to the understanding of earthquake and volcanic processes. AI can also be utilized for real-time monitoring, processing real-time data from various sensors to detect precursors or unusual activity. Additionally, AI can contribute to the development of early warning systems, providing timely alerts and information to potentially affected areas. However, it is crucial to recognize that AI should be used as a complementary tool to traditional scientific approaches, as accurate predictions and forecasting still require comprehensive geological knowledge and expert analysis. The abstract concludes by emphasizing that while AI shows promise, further research and collaboration are necessary to enhance our understanding and prediction capabilities for earthquakes and volcanic eruptions.

Keyphrases: AI, Volcanology Prediction, machine learning

@booklet{EasyChair:10432,
  author    = {Mostafa Hesham},
  title     = {Review: Enhancing Volcanology Prediction Capabilities Through Machine Learning and Data Analysis},
  howpublished = {EasyChair Preprint 10432},
  year      = {EasyChair, 2023}}