Related papers: A Parametric Framework for Anticipatory Flashflood…
The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment…
Forecasting compound floods presents a significant challenge due to the intricate interplay of meteorological, hydrological, and oceanographic factors. Analyzing compound floods has become more critical as the global climate increases flood…
Modern distributed systems employ aggressive optimization strategies that create latent risks - hidden vulnerabilities where exceptional performance masks catastrophic fragility when optimizations fail. Cache layers achieving 99% hit rates…
Short-term precipitation nowcasting is an inherently uncertain and under-constrained spatiotemporal forecasting problem, especially for rapidly evolving and extreme weather events. Existing generative approaches rely primarily on visual…
The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for…
Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast precipitation is therefore becoming more critical for…
Predicting crash events is crucial for understanding crash distributions and their contributing factors, thereby enabling the design of proactive traffic safety policy interventions. However, existing methods struggle to interpret the…
Extreme precipitation events occurring over large spatial domains pose substantial threats to societies because they can trigger compound flooding, landslides, and infrastructure failures across wide areas. A hybrid framework for spatial…
With extreme weather events becoming more common, the risk posed by surface water flooding is ever increasing. In this work we propose a model, and associated Bayesian inference scheme, for generating probabilistic (high-resolution…
Floods are increasingly frequent natural disasters causing extensive human and economic damage, highlighting the critical need for rapid and accurate flood inundation mapping. While remote sensing technologies have advanced flood monitoring…
As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accuracy are often insucient to characterize system reliability. This study proposes a…
Understanding the fundamental characteristics that shape the inherent flood risk disposition of urban areas is critical for integrated urban design strategies for flood risk reduction. Flood risk disposition specifies an inherent and…
Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on…
This study addresses the vital issue of real-time flood detection and management. It innovatively combines advanced deep learning models with Large language models (LLM), enhancing flood monitoring and response capabilities. This approach…
Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the generalized extreme-value (GEV) distribution within gauged and…
In this paper we discuss and address the challenges of predicting extreme atmospheric events like intense rainfall, hail, and strong winds. These events can cause significant damage and have become more frequent due to climate change.…
Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods…
Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and…
This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic…
As large language models (LLMs) are increasingly embedded in everyday decision-making, their safety responsibilities extend beyond reacting to explicit harmful intent toward anticipating unintended but consequential risks. In this work, we…