1 citations · 1 across the 7 of their papers we have counts for
8 papers
Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
Miguel Costa, Arthur Vandervoort, Carolin Schmidt +5
Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Desi…
Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
Miguel Costa, Arthur Vandervoort, Carolin Schmidt +4
Climate change is expected to intensify rainfall and other hazards, increasing disruptions in urban transportation systems. Designing effective adaptation strategies is challenging…
Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways
Miguel Costa, Arthur Vandervoort, Martin Drews +2
Climate change will cause an increase in the frequency and severity of flood events, prompting the need for cohesive adaptation policymaking. Designing effective adaptation policie…
Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
Miguel Costa, Arthur Vandervoort, Martin Drews +2
Urban flooding is expected to increase in frequency and severity as a consequence of climate change, causing wide-ranging impacts that include a decrease in urban Quality of Life (…
Domain-Adapted Pre-trained Language Models for Implicit Information Extraction in Crash Narratives
Xixi Wang, Jordanka Kovaceva, Miguel Costa +3
Free-text crash narratives recorded in real-world crash databases have been shown to play a significant role in improving traffic safety. However, large-scale analyses remain diffi…
Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
Xixi Wang, Miguel Costa, Jordanka Kovaceva +2
Large language models (LLMs) have shown promise in table Question Answering (Table QA). However, extending these capabilities to multi-table QA remains challenging due to unreliabl…