Related papers: Probabilistic Modeling of Hurricane Wind-Induced D…
In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage…
Windstorms are a primary natural hazard affecting Europe that are commonly linked to substantial property and infrastructural damage and are responsible for the largest spatially aggregated financial losses. Such extreme winds are typically…
In the United States, hurricanes are the most devastating natural disasters causing billions of dollars worth of damage every year. More importantly, construction jobsites are classified among the most vulnerable environments to severe wind…
This chapter addresses the increasing vulnerability of coastal regions to typhoons and the consequent power outages, emphasizing the critical role of power transmission systems in disaster resilience. It introduces a framework for assessing…
In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize assets damaged by these events so that the lost loads, especially sensitive and important loads,…
In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to…
While the hurdle Poisson regression is a popular class of models for count data with excessive zeros, the link function in the binary component may be unsuitable for highly imbalanced cases. Ordinary Poisson regression is unable to handle…
Offshore wind power (OWP) exhibits significant fluctuations under typhoon conditions, posing substantial challenges to the secure operation of power systems. Accurate forecasting of OWP is therefore essential. However, the inherent scarcity…
A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network…
It is shown that the physical damage resulting from severe weather phenomenon such as hurricanes and tornadoes encapsulated in various scales such as Saffir-Simpson, TORRO and the Enhanced Fujita Scale, and measured by the Power Dissipation…
Power outages caused by extreme weather events, such as hurricanes, can significantly disrupt essential services and delay recovery efforts, underscoring the importance of enhancing our infrastructure's resilience. This study investigates…
It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a…
To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model which enables integrated assessment of hazard, infrastructure system, and…
The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane…
The multifaceted nature of disaster impact shows that densely populated areas contribute more to aggregate burden, while sparsely populated but heavily affected regions suffer disproportionately at the individual level. This study…
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.…
Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster…
This study presents a hybrid neural network model for short-term (1-6 hours ahead) surface wind speed forecasting, combining Numerical Weather Prediction (NWP) with observational data from ground weather stations. It relies on the MeteoNet…
This study presents a framework for assessing urban critical infrastructure resilience during extreme events, such as hurricanes. The approach combines GIS and network analysis with open remote sensing data of the aftermath, vector data on…
Despite its importance for insurance, there is almost no literature on statistical hail damage modeling. Statistical models for hailstorms exist, though they are generally not open-source, but no study appears to have developed a stochastic…