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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]

Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought "truth." To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research-especially evaluation of historical and potential future changes in flash drought characteristics-should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information. SIGNIFICANCE STATEMENT: Rapid onset or "flash" drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research-especially that which reflects on historical or projected changes in flash drought characteristics-should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator. [ABSTRACT FROM AUTHOR]

Although study abroad trips provide an opportunity for affective and cognitive learning, it is largely assumed that they improve learning outcomes. The purpose of this study is to determine whether a study abroad field trip improved cognitive learning by comparing test performance between the study abroad participants (n = 20) and their peers who did not participate (n = 365). Test performance was statistically identical between these groups before and immediately after the study abroad program. On the final exam, the study abroad participants scored significantly higher. Qualitative methods were used to identify increased engagement with the course material and the creation of new social networks as likely explanations. (Contains 3 tables and 1 figure.)

Assessment is a key component of the learning process. In order to maximize student learning, instructors need to provide students with appropriate, explicit and timely assessment. Despite the potential benefits of problem-based learning, some instructors may be reluctant to adopt this approach because it will require them to modify their current assessment practices. In problem-based learning curricula, assessment should be used to encourage and reward student participation throughout the course. This paper presents an analytical rubric developed for diagnosis of student learning via formative assessment in a capstone environmental impact analysis course. The use and role of the analytical rubric in student grading is also explained. This rubric provides a template for how student learning could be assessed in other courses in geography curricula that employ a similar student-centred approach to learning. (Contains 5 tables.)