Citation :

Ryan B. Jaucian, Alonica R. Villanueva, "Volcanic Activity Detection through Plume Image Analysis using CNN-LSTM," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 261-2668, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P119

Abstract

Volcanic plumes pose serious risks to aviation, public health, and the environment, and they are essential visual indicators of a volcano's eruptive activity. Effective hazard mitigation and early warning systems depend on the timely and precise classification of plume types (such as ash, steam, or gas) and the forecasting of plume direction. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms are employed in this study to examine the use of a parallel machine learning model that analyzes volcanic plume imagery and predicts plume direction based on temporal and spatial features. Sequential image data was used to train the suggested parallel CNN-LSTM architecture, which allowed the model to predict directional movement over time and classify different types of plumes In addition to a directional prediction performance indicated by 0.1252 Mean Absolute Error (MAE) and 0.9217 R2, the experimental results showed high accuracy across tasks, with the model achieving a precision of 1.0000 for ash plumes, 0.9954 for steam plumes, and 0.9787 for gas plumes. The findings demonstrate the model's potential as a real-time volcanic plume monitoring application, making a substantial contribution to automated early warning systems and risk assessment techniques.

Keywords

Classification, Forecasting, Image Processing, Machine Learning, Plumes.

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