{"title":"A systemic approach to complex landslide risk reduction in coastal tourist areas: The case of Sirolo, central Italy","authors":"Elisa Mammoliti , Eleonora Gioia , Davide Fronzi , Adriano Mancini , Giorgio Mattioli , Roberta Bonì , Fabrizio Pontoni , Stefano Marabini , Stefano Mazzoli , Alberto Tazioli , Alessandra Negri","doi":"10.1016/j.ijdrr.2025.105810","DOIUrl":null,"url":null,"abstract":"<div><div>Coastal landslides in structurally complex and tourist-rich areas, such as the Monte Conero promontory in central Italy, pose significant challenges to environmental stability and public safety. In these settings, complex landslides often involve deep-seated translational movements evolving into sudden debris collapses at the footslope, generating significant hazards in beach areas heavily frequented by tourists. This study develops a conceptual, transferable model for complex landslide behavior in the Sirolo coastal sector in an integrated Hazard–Exposure–Vulnerability framework. The hazard component is investigated by systematically integrating multi-source data, including COSMO-SkyMed and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) data, <em>in-situ</em> inclinometer and piezometric time series, and high-resolution geological and geomorphological field mapping. Rainfall analyses and an Extreme Rainfall Periodic Index (ERPI) were used to explore the correlation between precipitation distribution and kinematic responses of the slope, offering an empirical insight into seasonal hazard modulation. To assess exposure, we developed a deep learning model to estimate beach attendance using limited Google Earth imagery (5 useable acquisition dates, moderate resolution), calibrated with regional tourism statistics, enabling a spatially and temporally explicit assessment of human presence. Finally, to explore vulnerability, a face-to-face survey was carried out to document gaps in visitor awareness of risk and civil-protection procedures. This integrated approach offers enhanced predictive capacity and supports targeted mitigation and communication measures in coastal environments characterized by complex landslides and intense seasonal human activity. To the authors knowledge, this is the first study integrating geological monitoring, rainfall indices, satellite data, deep learning-based exposure, and tourist risk perception into a systemic coastal landslide risk framework.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"130 ","pages":"Article 105810"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221242092500634X","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Coastal landslides in structurally complex and tourist-rich areas, such as the Monte Conero promontory in central Italy, pose significant challenges to environmental stability and public safety. In these settings, complex landslides often involve deep-seated translational movements evolving into sudden debris collapses at the footslope, generating significant hazards in beach areas heavily frequented by tourists. This study develops a conceptual, transferable model for complex landslide behavior in the Sirolo coastal sector in an integrated Hazard–Exposure–Vulnerability framework. The hazard component is investigated by systematically integrating multi-source data, including COSMO-SkyMed and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) data, in-situ inclinometer and piezometric time series, and high-resolution geological and geomorphological field mapping. Rainfall analyses and an Extreme Rainfall Periodic Index (ERPI) were used to explore the correlation between precipitation distribution and kinematic responses of the slope, offering an empirical insight into seasonal hazard modulation. To assess exposure, we developed a deep learning model to estimate beach attendance using limited Google Earth imagery (5 useable acquisition dates, moderate resolution), calibrated with regional tourism statistics, enabling a spatially and temporally explicit assessment of human presence. Finally, to explore vulnerability, a face-to-face survey was carried out to document gaps in visitor awareness of risk and civil-protection procedures. This integrated approach offers enhanced predictive capacity and supports targeted mitigation and communication measures in coastal environments characterized by complex landslides and intense seasonal human activity. To the authors knowledge, this is the first study integrating geological monitoring, rainfall indices, satellite data, deep learning-based exposure, and tourist risk perception into a systemic coastal landslide risk framework.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.