Examinando por Materia "environmental assessment"

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    Assessing and managing scenery of the Caribbean Coast of Colombia
    (Butterworth-Heinemann, 2013-01-01) Rangel-Buitrago, N.; Correa, I.D.; Anfuso, G.; Ergin, A.; Williams, A.T.; Universidad EAFIT. Departamento de Geología; Ciencias del Mar
    This study provides the coastal scenery assessment of 135 sites along the Colombian Caribbean littoral by analysing 26 physical and human factors. Sites were categorised into five classes from Class 1, top grade scenery, to Class 5, poor scenery. Fifty five percent of the investigated coastal areas were included in Classes 1 and 2, 18% belonged to Class 3 and 47% of the sites fall into Classes 4 and 5. Classification of analysed sites depends on the geological setting and the degree of human occupation. Classes 1 and 2 sites are located in natural protected areas in La Guajira and Magdalena departments. Low classification recorded at Classes 3, 4 and 5 corresponds to a progressive decrease of both natural and (especially) human parameters. Concerning coastal management issues, emphasis should be given to the upgrading of human parameters eliminating litter and sewage evidences, vegetation debris and enhancing beach nourishment works. © 2012 Elsevier Ltd.
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    Directional multivariate extremes in environmental phenomena
    (John Wiley and Sons Ltd, 2017-03-01) Torres, R.; De michele, C.; Laniado, H.; Lillo, R.E.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Several environmental phenomena can be described by different correlated variables that must be considered jointly in order to be more representative of the nature of these phenomena. For such events, identification of extremes is inappropriate if it is based on marginal analysis. Extremes have usually been linked to the notion of quantile, which is an important tool to analyze risk in the univariate setting. We propose to identify multivariate extremes and analyze environmental phenomena in terms of the directional multivariate quantile, which allows us to analyze the data considering all the variables implied in the phenomena, as well as look at the data in interesting directions that can better describe an environmental catastrophe. Because there are many references in the literature that propose extremes detection based on copula models, we also generalize the copula method by introducing the directional approach. Advantages and disadvantages of the nonparametric proposal that we introduce and the copula methods are provided in the paper. We show with simulated and real data sets how by considering the first principal component direction we can improve the visualization of extremes. Finally, two cases of study are analyzed: a synthetic case of flood risk at a dam (a three-variable case) and a real case study of sea storms (a five-variable case). Copyright © 2017 John Wiley & Sons, Ltd.