Preprints
https://doi.org/10.5194/essd-2024-44
https://doi.org/10.5194/essd-2024-44
23 Feb 2024
 | 23 Feb 2024
Status: a revised version of this preprint was accepted for the journal ESSD.

A field-based thickness measurement dataset of fallout pyroclastic deposits in the peri-volcanic areas of Campania region (Italy): Statistical combination of different predictions for spatial thickness estimation

Pooria Ebrahimi, Fabio Matano, Vincenzo Amato, Raffaele Mattera, and Germana Scepi

Abstract. Determining spatial thickness (z) of fallout pyroclastic deposits plays a key role in volcanological studies and shedding light on geomorphological and hydrogeological processes. However, this is a challenging line of research because: (1) field-based measurements are expensive and time-consuming; (2) the ash might have been dispersed in the atmosphere by several volcanic eruptions; and (3) wind characteristics during an eruptive event and soil-forming/denudation processes after ash deposition on the ground surface affect the expected spatial distribution of the fallout pyroclastic deposits. This article tries to bridge this knowledge gap by applying statistical techniques for making representative predictions. First, we compiled a field-based thickness measurement dataset (https://doi.org/10.5281/zenodo.8399487; Matano et al., 2023) of fallout pyroclastic deposits in several municipalities of Campania region, southern Italy. Second, 18 predictor variables were derived mainly from digital elevation models and satellite imageries and assigned to each measurement point. Third, the stepwise regression (STPW) model and random forest (RF) machine learning technique are used for thickness modeling. Fourth, the estimations are compared with those of three models that already exist in the literature. Finally, the statistical combination of different predictions is implemented to develop a less biased model for estimating pyroclastic thickness. The results show that prediction accuracy of RF (RMSE < 82.46 and MAE < 48.36) is better than that existing literature models. Moreover, statistical combination of the predictions obtained from the above-mentioned models through Least Absolute Deviation (LAD) combination approach leads to the most representative thickness estimation (MAE < 45.12) in the study area. The maps for the values estimated by RF and LAD (as the best single model and combination approach, respectively) illustrate that the spatial patterns did not alter significantly, but the estimations by LAD are smaller. This combined approach can help in estimating thickness of fallout pyroclastic deposits in other volcanic regions and in managing geohazards in areas covered with loose pyroclastic materials.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Pooria Ebrahimi, Fabio Matano, Vincenzo Amato, Raffaele Mattera, and Germana Scepi

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-44', Anonymous Referee #1, 21 Mar 2024
  • RC2: 'Comment on essd-2024-44', Anonymous Referee #2, 03 May 2024
  • AC1: 'Comment on essd-2024-44', Fabio Matano, 27 May 2024
Pooria Ebrahimi, Fabio Matano, Vincenzo Amato, Raffaele Mattera, and Germana Scepi

Data sets

Database of pyroclastic cover deposit thickness measurements (PT-Cam) in peri-volcanic areas of Campania (Italy) F. Matano et al. https://doi.org/10.5281/zenodo.8399487

Pooria Ebrahimi, Fabio Matano, Vincenzo Amato, Raffaele Mattera, and Germana Scepi

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Short summary
Fallout pyroclastic deposits cover hillslopes after explosive volcanic eruptions and strongly influence landscape evolution, hydrology, erosion, and slope stability processes. Accurate mapping the thickness spatial variations of these fallout pyroclastic deposits over large hillslope areas remains a knowledge gap. We attempt to bridge this gap by applying statistical techniques on a field-based thickness measurement dataset for making representative predictions.
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