Leveraging Machine Learning techniques and SEVIRI data to detect volcanic clouds composed of ash, ice, and SO₂

Abstract. Volcanic clouds can influence the climate and pose a serious threat to air transportation. Detecting and distinguishing them from meteorological clouds is particularly challenging because they often are composed of water vapor and ice particles, along with ash and gases. This study presents a Neural Network (NN) model for the detection of volcanic clouds composed of ash, ice, and SO₂, applied to data acquired by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite instrument. A dataset of 1.259 SEVIRI images related to Etna volcano eruptions spanning from 2020 to 2022, as well as 2024, was considered. The NN model, based on a multi-layer perceptron (MLP), was developed using 13 features, including thermal infrared channels and brightness temperature differences (BTD’s). The model was validated on three eruptive events not used in the training phase, demonstrating an overall high accuracy of 99 %, a precision >89 %, a recall >74 % and excellent capability to detect volcanic clouds, even in complex scenarios of high meteorological cloud cover. The results are promising for automatic and near-real-time detection of volcanic clouds, including those containing ice, and for improving retrieval processes.