**6. Applications of machine learning to other volcanological data**

ML appears more and more often in volcanology literature, and specific fields of application span now also other sub-disciplines.

Mount Erebus in Antarctica has a persistent lava lake showing Strombolian activity, but its location is definitely remote. Therefore, automatic methods to detect these explosions are highly needed. A CNN was trained using infrared images captured from the crater rim and "labeled" with the help of accompanying seismic data, which was not used anymore during the subsequent automatic detection [146].

Clast morphology is a fundamental tool also for studies concerning volcanic textures. Texture analysis of clasts provides in particular information about genesis, transport and depositional processes. Here, ML has still to be developed fully but e.g., the application of preprocessing techniques such as the Radon transform can be a first step towards an efficient definition of feature vectors to be used for classification, as shown e.g., at Colima volcano [147].

The Museum of Mineralogy, Petrography and Volcanology of the University of Catania implemented a communication system based on the visitor's personal experience to learn by playing. There is a web application called I-PETER: Interactive Platform to Experience Tours and Education on the Rocks. This platform includes a labeled dataset of images of rocks and minerals to be used also for petrological investigations based on ML [148].

Satellite remote sensing technology is increasingly used for monitoring the surface of the Earth in general, and volcanoes in particular, especially in areas where ground monitoring is scarce or completely missing. For instance, in Latin America 202 out of 319 Holocene volcanoes did not have seismic, deformation or gas monitoring in 2013 [7]. A complex-valued CNN was proposed to extract areas with land shapes similar to given samples in interferometric synthetic aperture radar (InSAR), a technique widely applied in volcano monitoring. An application was presented grouping similar small volcanoes in Japan [149]. InSAR measurements have great potential for volcano monitoring, especially where images are freely available. ML methods can be used for the initial processing of single satellite data. Processing of potential unrest areas can then fully exploit integrated multi-disciplinary, multisatellite datasets [7]. The Copernicus Programme of the European Space Agency (ESA) and the European Union (EU) has recently contributed by producing the Sentinel-2 multispectral satellites, able to provide high resolution satellite data for disaster monitoring, as well as complementing previous satellite images like Landsat. The free access policy also promotes an increasing use of Sentinel-2 data, which is often processed by ML techniques such as SVM and RF [150]. A transfer learning strategy was applied to ground deformation in Sentinel-1 data [151] and a range of pretrained networks was tested, finding that AlexNet [152] is best suited to this task. The positive results were checked by a researcher and fed back for model updating.

The global volcano monitoring platform MOUNTS (Monitoring Unrest from Space) uses multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogs), and CNN to provide support for volcanic risk assessment. Results are visualized on an open-access website. The efficiency of the system was tested on several eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019) [153].

Debris flow events are one of the most widespread and dangerous natural processes not only on volcanoes but more in general in mountainous environments. A methodology was recently proposed [154] that combines the results of deterministic and heuristic/probabilistic models for susceptibility assessment. RF models are extensively used to represent the heuristic/probabilistic component of the modeling. The case study presented is given by the Changbai Shan volcano, China [154].

Mapping lava flows from satellite is another important remote sensing application. RF was applied to 20 individual flows and 8 groups of flows of similar age using a Landsat 8 image and a DEM of Nyamuragira (Congo) with 30 m resolution. Despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification [155].

The hazard related to landslides at volcanoes is also significant. DNN models were proposed for landslide susceptibility assessment in Viet Nam, showing considerable better performance with respect to other ML methods such as MLP, SVM, DT and RF [156]. The use of DNN approach could be therefore an interesting approach for the landslide susceptibility mapping of active volcanoes.

Muon imaging has been successfully used by geophysicists to investigate the internal structure of volcanoes, for example at Etna (Italy) [157]. Muon imaging is essentially an inverse problem and it can profit from the application of ML techniques, such as ANN and CA [158].

Combinations of supervised and unsupervised ML techniques have been used to map volcanoes also on other planets. A ML paradigm was designed for the identification of volcanoes on Venus [159]. Other studies have used topographic data, such as DEM and associated derivatives obtained from orbital images, to detect and classify manually labeled Martian landforms including volcanoes [160].

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*Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

ML techniques will have an increasing impact on how we study and model volcanoes in all their aspects, how we monitor them and how we evaluate their hazards, both in the short and in the long term. The increasing number of monitoring equipment installed on volcanoes on one side provides more and more data, on the other often causes their real time processing unfeasible especially when most needed i.e., during unrest and eruptions. Here ML will show its best usefulness, as it can provide the perfect tools to sift through big data to identify subtle patterns that could indicate unrest, hopefully well before eruptions. One important issue is the one of generalization. We must go towards the construction of ML models that can be applied on different volcanoes, for instance when previous data is not available for training specific models. The concepts of transfer learning can

The routine use of ML tools at the different volcano observatories should be promoted by providing easy installation procedures and easy integration into existing monitoring systems. Open source software should be always chosen whenever possible. On the other hand, observatories should provide good open training data to ML developers, researchers and data scientists in order to improve the models in a virtuous circle. An easy availability of open access data, both from the ground and from satellites should be exploited for building reliable training sets in the different fields of volcanology. This will allow "scientific competition" between research groups using different ML approaches and make a direct comparison of results easier, like it is common in other disciplines where "standard"

RC wishes to acknowledge the invaluable help resulted from discussions with his coauthors during previous works; in particular, collaborations with Luca Barbui, Moritz Beyreuther, Corentin Caudron, Guillermo Cortés, Art Jolly, Philippe Lesage,

This review is partially based on the results of a previous project funded under the European Union's Horizon 2020 research and innovation program under the

Marie Skłodowska-Curie Grant Agreement No. 749249 (VULCAN.ears).

training datasets are available for download to everybody.

The authors declare no conflict of interest.

**7. Conclusions**

be important here.

**Acknowledgements**

Joachim Wassermann.

**Conflict of interest**
