{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:21:26Z","timestamp":1773703286732,"version":"3.50.1"},"reference-count":111,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Interreg Italy-Croatia Programme 2021-27"},{"name":"RESONANCE (impRoving landslidE riSk preventiOn aNd mAnagement iN Coastal arEas)"},{"name":"University of Urbino"},{"name":"EMAS PhD funds"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing\/applying machine learning algorithms and\/or numerical modeling, in order to identify\/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological\/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height.<\/jats:p>","DOI":"10.3390\/rs16142604","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T08:48:29Z","timestamp":1721206109000},"page":"2604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detection of Cliff Top Erosion Drivers through Machine Learning Algorithms between Portonovo and Trave Cliffs (Ancona, Italy)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9873-7219","authenticated-orcid":false,"given":"Nicola","family":"Fullin","sequence":"first","affiliation":[{"name":"Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, Italy"},{"name":"Italian National Research Council (CNR), Research Institute for Geo-Hydrological Protection (IRPI), Corso Stati Uniti 4, 35127 Padova, Italy"}]},{"given":"Michele","family":"Fraccaroli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6358-374X","authenticated-orcid":false,"given":"Mirko","family":"Francioni","sequence":"additional","affiliation":[{"name":"Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy"}]},{"given":"Stefano","family":"Fabbri","sequence":"additional","affiliation":[{"name":"Department of Biological, Geological and Environmental Sciences BiGeA, University of Bologna, Via Zamboni 33, 40126 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8750-1177","authenticated-orcid":false,"given":"Angelo","family":"Ballaera","sequence":"additional","affiliation":[{"name":"Italian National Research Council (CNR), Research Institute for Geo-Hydrological Protection (IRPI), Corso Stati Uniti 4, 35127 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-8185","authenticated-orcid":false,"given":"Paolo","family":"Ciavola","sequence":"additional","affiliation":[{"name":"Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8707-9158","authenticated-orcid":false,"given":"Monica","family":"Ghirotti","sequence":"additional","affiliation":[{"name":"Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.ecolecon.2006.10.022","article-title":"The Coasts of Our World: Ecological, Economic and Social Importance","volume":"63","author":"Intralawan","year":"2007","journal-title":"Ecol. 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