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ENBIS-21 Online Conference

13–15 Sept 2021
Online
Europe/Berlin timezone

Session

Modelling 6

PS73_C18
15 Sept 2021, 14:00
Online

Online

Conveners

Modelling 6

  • Chair: András Zempléni (Eötvös Loránd University, Budapest)

Presentation materials

There are no materials yet.

  1. 27. Study of the effectiveness of Bayesian kriging for the decommissioning and dismantling of nuclear sites.
    Martin Wieskotten (CEA)
    15/09/2021, 14:00
    Modelling

    The decommissioning of nuclear infrastructures such as power plants arises as these facilities age and come to the end of their lifecycle. The decommissioning projects expect a complete radiological characterization of the site, of both the soil and the civil engineering structure to optimize efficiency and minimize the costs of said project. To achieve such goal, statistical tools such as...

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  2. 82. Machine Learning Approach to Predict Land Prices using Spatial Dependency Factors
    Mr Supun Delpagoda (Department of Physical Sciences, Faculty of Applied Sciences, Rajarata University of Sri Lanka.), Dr Kaushalya Premachandra (Department of Physical Sciences, Faculty of Applied Sciences, Rajarata University of Sri Lanka.), Mr Ranjan Dissanayake (Department of Physical Sciences, Faculty of Applied Sciences, Rajarata University of Sri Lanka.)
    15/09/2021, 14:20
    Other/special session/invited session

    In real estate models, spatial variation is an important factor in predicting land prices. Spatial dependency factors (SDFs) under spatial variation play a key role in predicting land prices. The objective of this study was to develop a novel real estate model that is suitable for Sri Lanka by exploring the factors affecting the prediction of land prices using ordinary least squares regression...

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  3. 112. IMPORTANCE OF SPATIAL DEPENDENCE IN THE CLUSTERING OF NDVI FUNCTIONAL DATA ACROSS THE ECUADORIAN ANDES
    Mr Jeysson Chuquin (Escuela Politécnica Nacional), Ms Alexandra Maigua (Escuela Politécnica Nacional)
    15/09/2021, 14:40
    Modelling

    The spatial dependence on environmental data is an influential criterion in clustering processes, since the results obtained provide relevant information. As classical methods do not consider spatial dependence, considering this structure produces unexpected results, and groupings of curves that cannot be similar in shape/behavior.
    In this work, the clustering is performed using the modified...

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