Wigington, Parker J., Jr., Scott G. Leibowitz, Randy L. Comeleo, and Joseph L. Ebersole, 2012. Oregon Hydrologic Landscapes: A Classification Framework. Journal of the American Water Resources Association (JAWRA) 1‐20. DOI: 10.1111/jawr.12009 Abstract: There is a growing need for hydrologic classification systems that can provide a basis for broad‐scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classification approach that describes factors of climate‐watershed systems that control the hydrologic characteristics of watersheds. Our assessment units are incremental watersheds (i.e., headwater watersheds or areas draining directly into stream reaches). Major components of the classification include indices of annual climate, climate seasonality, aquifer permeability, terrain, and soil permeability. To evaluate the usefulness of our approach, we identified 30 rivers with long‐term streamflow‐gauging records and without major diversions and impoundments. We used statistical clustering to group the streams based on the shapes of their annual hydrographs. Comparison of the streamflow clusters and HL distributions within river basin clusters shows that the Oregon HL approach has the ability to provide insights about the expected hydrologic behavior of HLs and larger river basins. The Oregon HL approach has potential to be a useful framework for comparing hydrologic attributes of streams and rivers in the Pacific Northwest. 相似文献
Abstract: Freshwater biodiversity is highly endangered and faces increasing threats worldwide. To be complete, regional plans that identify critical areas for conservation must capture representative components of freshwater biodiversity as well as rare and endangered species. We present a spatially hierarchical approach to classify freshwater systems to create a coarse filter to capture representative freshwater biodiversity in regional conservation plans. The classification framework has four levels that we described using abiotic factors within a zoogeographic context and mapped in a geographic information system. Methods to classify and map units are flexible and can be automated where high-quality spatial data exist, or can be manually developed where such data are not available. Products include a spatially comprehensive inventory of mapped and classified units that can be used remotely to characterize regional patterns of aquatic ecosystems. We provide examples of classification procedures in data-rich and data-poor regions from the Columbia River Basin in the Pacific Northwest of North America and the upper Paraguay River in central South America. The approach, which has been applied in North, Central, and South America, provides a relatively rapid and pragmatic way to account for representative freshwater biodiversity at scales appropriate to regional assessments. 相似文献
Objective: The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques.
Methods: This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007–2013 to identify VRU injury severity factors at HRGCs.
Results: The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.
Conclusions: The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs. 相似文献
This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization. 相似文献