Andrea Marinoni
Biographic Data
| ID | 7993153 |
|---|---|
| NAME | Andrea Marinoni |
| GIVEN NAMES | Andrea |
| FAMILY NAME | Marinoni |
| SIGNATURE | MARINONI A |
| AFFILIATIONS | University of Cambridge |
| ORCID | 0000-0001-6789-0915 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Machine learning for gap‐filling in greenhouse gas emissions databases
Greenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and pro…
Enhancing assessment of direct and indirect exposure of settlement-transportation systems to mass movements by intergraph representation learning
Amidst the intensifying extreme rainfall patterns due to climate change, global early warning systems for mass movements (e.g., landslides, avalanches) need to provide not only the coarsely aggregated danger reports, but also the necessary fine details to understand its potential implications on critical infrastructures such as transportation systems. In this study, we introduce a novel ``intergraph'' method that enhances the exposure information…
No prominent works on this page.
Machine learning for gap‐filling in greenhouse gas emissions databases
Greenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and pro…
Enhancing assessment of direct and indirect exposure of settlement-transportation systems to mass movements by intergraph representation learning
Amidst the intensifying extreme rainfall patterns due to climate change, global early warning systems for mass movements (e.g., landslides, avalanches) need to provide not only the coarsely aggregated danger reports, but also the necessary fine details to understand its potential implications on critical infrastructures such as transportation systems. In this study, we introduce a novel ``intergraph'' method that enhances the exposure information…
Computer Science (2 works) · Artificial Intelligence (1 work) · Atmospheric and Environmental Gas Dynamics (1 work) · Data mining (1 work) · Data Quality and Management (1 work) · Data science (1 work) · Database (1 work) · Environmental planning (1 work) · Environmental Science (1 work) · Exploit (1 work)