Maria Polese
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Maria Polese has a total of 122 co-authors. Here are the top co-authors by number of shared publications:
- Andrea Prota
Sezione INFN di Napoli
14 shared publications - Gaetano Manfredi
University of Naples Federico II
12 shared publications - Marco Gaetani D'ARagona
University of Naples “Federico II
12 shared publications - Gerardo Mario Verderame
Department of Structures for Engineering and Architecture, University of Naples Federico II, Naples, Italy
11 shared publications - Gabriella Tocchi
University of Naples Federico II, Department of Structures for Engineering and Architecture, Naples, Italy
9 shared publications - Marco Di Ludovico
Department of Structures for Engineering and Architecture, University of Naples Federico II, Naples, Italy
8 shared publications
Latest Publications
- 13 October 2026
- Published by Springer Nature
- p. 433-443
A Multi-scale Framework for Assessing Multi-hazard Risk in Urban Settlements
- 13 October 2026
- Published by Springer Nature
- p. 433-443
A Multi-scale Framework for Assessing Multi-hazard Risk in Urban Settlements
- 13 October 2026
- Published by Springer Nature
- p. 219-228
Image-Based and Data-Driven Seismic Exposure Modelling Using CNNs: Application to Municipality of Podgorica
- 13 October 2026
- Published by Springer Nature
- p. 219-228
Image-Based and Data-Driven Seismic Exposure Modelling Using CNNs: Application to Municipality of Podgorica
- 13 October 2026
- Published by Springer Nature
- p. 501-511
A Mechanics-Based Method for Assessing Combined Seismic and Pyroclastic Density Current Effects on RC Buildings
- 13 October 2026
- Published by Springer Nature
- p. 501-511
A Mechanics-Based Method for Assessing Combined Seismic and Pyroclastic Density Current Effects on RC Buildings
- Open Access
- 26 June 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 143
Virtual testbed for multi-risk assessment: application to the Inland RETURNVILLE
- 26 June 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 143
Open AccessVirtual testbed for multi-risk assessment: application to the Inland RETURNVILLE
- Open Access
- 4 June 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 142
Multi-hazard risk assessment in coastal urban settlements: application to the RETURNVILLE virtual testbed
- 4 June 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 142
Open AccessMulti-hazard risk assessment in coastal urban settlements: application to the RETURNVILLE virtual testbed
- Top Cited PapersOpen Access
- 27 May 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 142
Virtual testbed for multi-risk assessment: defining RETURNVILLEs to support the analysis and testing of DRM and CCA solutions in realistic urban contexts
- 27 May 2026
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 142
Top Cited PapersOpen AccessVirtual testbed for multi-risk assessment: defining RETURNVILLEs to support the analysis and testing of DRM and CCA solutions in realistic urban contexts
- Open Access
- 16 July 2025
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 128
A tool to assess road interruption due to earthquake-induced collapse of infill walls in RC buildings
- 16 July 2025
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 128
Open AccessA tool to assess road interruption due to earthquake-induced collapse of infill walls in RC buildings
- Open Access
- 16 April 2025
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 124
Integrating multi-hazard, socio-physical information in a holistic index for decision making on disaster risk reduction
- 16 April 2025
- Published by Elsevier in International Journal of Disaster Risk Reduction
- Vol. 124
Open AccessIntegrating multi-hazard, socio-physical information in a holistic index for decision making on disaster risk reduction
- Open Access
- 18 March 2025
- Published by Copernicus GmbH
Integrating Heterogeneous Datasets and Advanced Modelling Techniques for Multi-Hazard Risk Assessment in Urban Environments
As the effects of climate change, population growth, and urbanization intensify, there has been a surge in the frequency and severity of extreme natural hazards, often leading to catastrophic disasters. This escalating threat underscores the pressing need to devise and rigorously test innovative methodologies for risk assessment that consider the complex interactions between multiple hazards.This study aims to develop a comprehensive framework for multi-hazard risk analysis, with a focus on capturing the intricate dynamics between different systems, such as the built environment and human populations, within vulnerable urban settings. To this aim, the study examines specific zones within the urban areas of Palermo and Naples, Italy – two highly complex urban environments with significant populations and exposure to various natural hazards – along with the entire area of the small town of Giampilieri (Sicily). The town experienced significant impacts during the 2009 Messina floods and flow-like landslides, which resulted in at least 31 fatalities and left over 400 people homeless due to the collapse of numerous houses.The research integrates a diverse array of datasets from both institutional and non-institutional sources (e.g., open data), including historical hazard records, socioeconomic and demographic information, and various environmental variables. These datasets are utilized to simulate interactions among hazards, both in terms of physical phenomena (occurrence interactions) and their resulting impacts (consequence interactions). Probabilistic models and machine learning algorithms (e.g., Bayesian Networks, Random Forest) are explored to capture various hazard dependencies and cascading effects, offering deeper insights into potential impacts on communities and various infrastructure systems (e.g., building and transport networks).The ultimate goal is to develop scalable decision-support tools for disaster risk management and planning, enhancing resilience and supporting sustainable urban development.
- 18 March 2025
- Published by Copernicus GmbH
Open AccessIntegrating Heterogeneous Datasets and Advanced Modelling Techniques for Multi-Hazard Risk Assessment in Urban Environments
As the effects of climate change, population growth, and urbanization intensify, there has been a surge in the frequency and severity of extreme natural hazards, often leading to catastrophic disasters. This escalating threat underscores the pressing need to devise and rigorously test innovative methodologies for risk assessment that consider the complex interactions between multiple hazards.This study aims to develop a comprehensive framework for multi-hazard risk analysis, with a focus on capturing the intricate dynamics between different systems, such as the built environment and human populations, within vulnerable urban settings. To this aim, the study examines specific zones within the urban areas of Palermo and Naples, Italy – two highly complex urban environments with significant populations and exposure to various natural hazards – along with the entire area of the small town of Giampilieri (Sicily). The town experienced significant impacts during the 2009 Messina floods and flow-like landslides, which resulted in at least 31 fatalities and left over 400 people homeless due to the collapse of numerous houses.The research integrates a diverse array of datasets from both institutional and non-institutional sources (e.g., open data), including historical hazard records, socioeconomic and demographic information, and various environmental variables. These datasets are utilized to simulate interactions among hazards, both in terms of physical phenomena (occurrence interactions) and their resulting impacts (consequence interactions). Probabilistic models and machine learning algorithms (e.g., Bayesian Networks, Random Forest) are explored to capture various hazard dependencies and cascading effects, offering deeper insights into potential impacts on communities and various infrastructure systems (e.g., building and transport networks).The ultimate goal is to develop scalable decision-support tools for disaster risk management and planning, enhancing resilience and supporting sustainable urban development.