Résultats de la recherche | Land Portal

Résultats de la recherche

Showing items 1 through 9 of 3.
  1. Library Resource

    Volume 10 Issue 2

    Publication évaluée par des pairs
    février, 2021
    Canada, Chine, États-Unis d'Amérique

    There is growing evidence that exposure to nature increases human well-being, including in urban areas. However, relatively few studies have linked subjective satisfaction to objective features of the environment. In this study we explore the links among objective environmental features (tree cover, water, and bird diversity) and subjective judgements of satisfaction. We surveyed residents of Ottawa, Canada (n = 1035) about their satisfaction with their local neighbourhoods.

  2. Library Resource

    Volume 10 Issue 1

    Publication évaluée par des pairs
    janvier, 2021
    Australie, Belgique, Canada, Chine, Fédération de Russie, États-Unis d'Amérique

    Though forest ecosystems play a critical role in enhancing ecological, environmental, economic, and societal sustainability, on a global scale, their future outlooks are uncertain given the wide-ranging threats they are exposed to. The uniqueness of this study is to provide a line of evidence in which forest change trajectories are not only tracked but also evaluated through the lenses of forestry and economic oriented events’ timelines. The dynamics of forest change trajectories were mined using a temporal model.

  3. Library Resource

    Volume 9 Issue 7

    Publication évaluée par des pairs
    juillet, 2020
    Canada, Chine, Fédération de Russie, Suède, États-Unis d'Amérique

    Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E.

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