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Coastal typology development with heterogeneous data sets 总被引:3,自引:0,他引:3
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Amanda S. Hering Cynthia L. Bell Marc G. Genton 《Environmental and Ecological Statistics》2009,16(2):225-250
We analyze and model the structure of spatio-temporal wildfire ignitions in the St. Johns River Water Management District
in northeastern Florida. Previous studies, based on the K-function and an assumption of homogeneity, have shown that wildfire events occur in clusters. We revisit this analysis based
on an inhomogeneous K-function and argue that clustering is less important than initially thought. We also use K-cross functions to study multitype point patterns, both under homogeneity and inhomogeneity assumptions, and reach similar
conclusions as above regarding the amount of clustering. Of particular interest is our finding that prescribed burns seem
not to reduce significantly the occurrence of wildfires in the current or subsequent year over this large geographical region.
Finally, we describe various point pattern models for the location of wildfires and investigate their adequacy by means of
recent residual diagnostics.
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Marc G. Genton (Corresponding author)Email: Email: |
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Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness into subjective judgments. Afterward, failure modes were clustered by developing a novel k-means clustering algorithm that accepts PFNs as input. Finally, the PF-VIKOR approach was used to analyze the ordering of cluster risks. The proposed approach was implemented in the dehydration unit of an Iranian gas refinery and the results were compared with the traditional FMEA. The findings showed the flexibility and applicability of the proposed approach in addressing real-world problems. This research provides two key contributions: (1) designing a PFN-based k-means clustering algorithm that tackles FMEA limitations and (2) using the PF-VIKOR method for prioritizing and evaluating failure mode clusters. 相似文献
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城市大气采样点的模糊聚类优选方法 总被引:3,自引:0,他引:3
运用数理统计模糊聚类分析方法对各大气采样点获得的监测数据进行聚类相关分析,优选出较科学、合理且足以能反映城市大气污染水平、分布特征及时空变化规律的采样点。文中详细介绍了优选方法。 相似文献
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Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets. 相似文献
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Sari Kontunen-Soppela Juha Parviainen Mikael Brosché Ramesh C. Thakur Jaakko Kangasjärvi David F. Karnosky 《Environmental pollution (Barking, Essex : 1987)》2010,158(4):959-968
Gene expression responses of paper birch (Betula papyrifera) leaves to elevated concentrations of CO2 and O3 were studied with microarray analyses from three time points during the summer of 2004 at Aspen FACE. Microarray data were analyzed with clustering techniques, self-organizing maps, K-means clustering and Sammon's mappings, to detect similar gene expression patterns within sampling times and treatments. Most of the alterations in gene expression were caused by O3, alone or in combination with CO2. O3 induced defensive reactions to oxidative stress and earlier leaf senescence, seen as decreased expression of photosynthesis- and carbon fixation-related genes, and increased expression of senescence-associated genes. The effects of elevated CO2 reflected surplus of carbon that was directed to synthesis of secondary compounds. The combined CO2 + O3 treatment resulted in differential gene expression than with individual gas treatments or in changes similar to O3 treatment, indicating that CO2 cannot totally alleviate the harmful effects of O3. 相似文献
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IntroductionBicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase. Method: Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009–2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes. Results: Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn’t be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity. 相似文献