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A wide variety of weather conditions, from windstorms to prolonged heat events, can substantially impact power systems, posing many risks and inconveniences due to power outages. Accurately estimating the probability distribution of the number of customers without power using data about the power utility system and environmental and weather conditions can help utilities restore power more quickly and efficiently. However, the critical shortcoming of current models lies in the difficulties of handling (i) data streams and (ii) model uncertainty due to combining data from various weather events. Accordingly, this article proposes an adaptive ensemble learning algorithm for data streams, which deploys a feature‐ and performance‐based weighting mechanism to adaptively combine outputs from multiple competitive base learners. As a proof of concept, we use a large, real data set of daily customer interruptions to develop the first adaptive all‐weather outage prediction model using data streams. We benchmark several approaches to demonstrate the advantage of our approach in offering more accurate probabilistic predictions. The results show that the proposed algorithm reduces the probabilistic predictions' error of the base learners between 4% and 22% with an average of 8%, which also result in substantially more accurate point predictions. The improvement made by our algorithm is enhanced as we exchange base learners with simpler models. [ABSTRACT FROM AUTHOR]

Data‐driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, "epistemic uncertainty." In this article, we demonstrate this point by conceptually linking the main elements and output of data‐driven predictive models with the main elements of a general risk description, thereby placing data‐driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data‐driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data‐driven predictive modeling. [ABSTRACT FROM AUTHOR]

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent‐based model (ABM) to simulate homeowners' adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long‐term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane—when their memory of the event is the strongest—it can help to substantially decrease the vulnerability of a community. [ABSTRACT FROM AUTHOR]

Large-scale land transactions (LSLTs) can precipitate dramatic changes in land systems. Ethiopia has experienced one of the largest amounts of LSLTs in Africa, yet their effects on local land systems are poorly understood. In this study, we quantify the direct and indirect land use and land cover (LULC) changes associated with LSLTs at eight socio-environmentally diverse sites in central and western Ethiopia. To estimate these effects, we employ a novel, two-stage counterfactual analysis. We first use a region-growing procedure to identify a "control" site with comparable landscape-level characteristics to each LSLT. Then, we sample and reweight points within each control site to further improve covariate balance. This two-stage approach both controls for potential confounding factors at multiple spatial levels and reduces the costs of extensive LULC data classification. Our results show that the majority of the reported transacted area (62%) remained unconverted to large-scale agriculture. Most of the land that was developed into large-scale agriculture displaced smallholder agriculture (53%), followed by conversion of woodland/shrubland (35%) and forest (9%). Beyond their boundaries, LSLTs indirectly influenced rates of smallholder agricultural expansion and abandonment, pointing to site dependence in how LSLTs affect adjacent land systems. In particular, the low prevalence of forest within and around these LSLTs underscores a need to move beyond measures of deforestation as proxies for LSLT effects on land systems. Our two-stage approach shows promise as an efficient method for generating robust counterfactuals and thereby LULC change estimates in systems lacking wall-to-wall LULC data. [ABSTRACT FROM AUTHOR]

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce., (© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

A wide variety of weather conditions, from windstorms to prolonged heat events, can substantially impact power systems, posing many risks and inconveniences due to power outages. Accurately estimating the probability distribution of the number of customers without power using data about the power utility system and environmental and weather conditions can help utilities restore power more quickly and efficiently. However, the critical shortcoming of current models lies in the difficulties of handling (i) data streams and (ii) model uncertainty due to combining data from various weather events. Accordingly, this article proposes an adaptive ensemble learning algorithm for data streams, which deploys a feature- and performance-based weighting mechanism to adaptively combine outputs from multiple competitive base learners. As a proof of concept, we use a large, real data set of daily customer interruptions to develop the first adaptive all-weather outage prediction model using data streams. We benchmark several approaches to demonstrate the advantage of our approach in offering more accurate probabilistic predictions. The results show that the proposed algorithm reduces the probabilistic predictions' error of the base learners between 4% and 22% with an average of 8%, which also result in substantially more accurate point predictions. The improvement made by our algorithm is enhanced as we exchange base learners with simpler models., (© 2023 Society for Risk Analysis.)

Data-driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, "epistemic uncertainty." In this article, we demonstrate this point by conceptually linking the main elements and output of data-driven predictive models with the main elements of a general risk description, thereby placing data-driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data-driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data-driven predictive modeling., (© 2023 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners' adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane-when their memory of the event is the strongest-it can help to substantially decrease the vulnerability of a community., (© 2022 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)