Saman Ghaffarian
Biographic Data
| ID | 8368339 |
|---|---|
| NAME | Saman Ghaffarian |
| GIVEN NAMES | Saman |
| FAMILY NAME | Ghaffarian |
| SIGNATURE | GHAFFARIAN S |
| AFFILIATIONS | National Science and Technology Center for Disaster Reduction |
| ORCID | 0000-0001-9882-4603 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Evaluating multi-hazard preparedness for a major earthquake
Tabriz, a key economic and political hub in Iran, is highly susceptible to a range of natural hazards, particularly earthquakes and landslides. This study develops a multi-hazard risk scenario – combining earthquake, triggered landslides, and their cascading impacts on road transportation – to assess the potential impact of a 7.3 magnitude earthquake on the city's infrastructure and emergency response systems. Using a GIS-based hazard model integ…
Digital post-disaster risk management twinning
Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the …
Explainable artificial intelligence in disaster risk management
Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding o…
Agent-based modelling of post-disaster recovery with remote sensing data
Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic process…
No prominent works on this page.
Agent-based modelling of post-disaster recovery with remote sensing data
Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic process…
Explainable artificial intelligence in disaster risk management
Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding o…
Digital post-disaster risk management twinning
Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the …
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Evaluating multi-hazard preparedness for a major earthquake
Tabriz, a key economic and political hub in Iran, is highly susceptible to a range of natural hazards, particularly earthquakes and landslides. This study develops a multi-hazard risk scenario – combining earthquake, triggered landslides, and their cascading impacts on road transportation – to assess the potential impact of a 7.3 magnitude earthquake on the city's infrastructure and emergency response systems. Using a GIS-based hazard model integ…
Business (3 works) · Computer Science (3 works) · Engineering (3 works) · Anomaly Detection Techniques and Applications (2 works) · Data science (2 works) · Disaster Management and Resilience (2 works) · Environmental planning (2 works) · Flood Risk Assessment and Management (2 works) · Geography (2 works) · Hazard (2 works)