Advanced AI-Based Techniques for Empirical Chronological Mapping of Lava Flows Using Multispectral Satellite Imagery: A Case Study of Mount Etna

Effusive volcanic eruptions pose significant hazards to populated areas, making the accurate dating of lava flows essential for reconstructing eruptive histories and assessing volcanic risk. Traditional dating techniques, such as radiometric, paleomagnetic and lichenometric methods, are highly reliable but require direct sampling, complex analytical procedures, and substantial financial resources. Satellite remote sensing provides a non-invasive and spatially extensive approach that can complement conventional methods in the regional investigation of exposed volcanic surfaces. This study presents a two-stage hybrid classification–regression framework for empirical age estimation and chronological mapping of historical lava surfaces on Mount Etna, spanning the 17th to the 21st centuries. In the first stage, a Random Forest Classifier distinguishes background, pre-1600 lava surfaces, and post-1600 historical lava surfaces, thereby defining the spatial applicability domain of the chronological model. In the second stage, a Random Forest Regressor estimates age only for pixels classified as post-1600 lava by integrating ten Sentinel-2 surface-reflectance bands, elevation derived from the Digital Elevation Model (DEM), and a binary north-flank spatial indicator. On the balanced pixel-level test dataset, the RFR achieved an R2 of 0.549, a Mean Absolute Error of 66.2 years, and a Root Mean Squared Error of 89.0 years. Using the median prediction for each test eruption, the corresponding eruption-level values were R2=0.701, MAE =51.6 years, and RMSE =67.2 years. Prediction errors were larger for the oldest evaluated surfaces, indicating reduced empirical discrimination near the lower boundary of the calibrated chronological domain. This pattern may reflect calibration-boundary effects, sparse chronological coverage, eruption-specific variability, and possible convergence of surface spectral properties. Comparison with an RBF-SVR showed comparable but metric-dependent performance, without establishing the overall superiority of either regression architecture. Permutation analysis indicated that most predictive information within this dataset was associated with the Sentinel-2 spectral component, particularly the Red Edge bands. The proposed method should therefore be interpreted as a complementary fast empirical lava age-estimation and regional screening framework for exposed historical lava surfaces, rather than as a substitute for radiometric, paleomagnetic, or field-based dating.