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A platform for research: civil engineering, architecture and urbanism
Wind speed prediction in a complex terrain
10.1002/env.480.abs
We present a novel method for analysing spatial data when response data is given at a finite number of locations and the aim is to predict the response at a new location, where only a short run of data is available. This is the type of dataset that is typically available when attempting to analyse wind velocity data. We demonstrate our method, and compare it to that introduced by Haslett and Raftery on a set of data collected from the island of Crete in Greece. Typically the distance between locations is used to define the correlation matrix between responses at distinct locations even though this cannot always be justified. The peculiarity presented in our data is that the sites are in a complex topography so differences in the local characteristics of the wind station the direction of the prevailing winds, and other unobserved covariates can all lead to unsuitable model fitting. We use a nonlinear model to avoid these problems and demonstrate its predictive power in relation to the dataset under study. Copyright © 2001 John Wiley & Sons, Ltd.
Wind speed prediction in a complex terrain
10.1002/env.480.abs
We present a novel method for analysing spatial data when response data is given at a finite number of locations and the aim is to predict the response at a new location, where only a short run of data is available. This is the type of dataset that is typically available when attempting to analyse wind velocity data. We demonstrate our method, and compare it to that introduced by Haslett and Raftery on a set of data collected from the island of Crete in Greece. Typically the distance between locations is used to define the correlation matrix between responses at distinct locations even though this cannot always be justified. The peculiarity presented in our data is that the sites are in a complex topography so differences in the local characteristics of the wind station the direction of the prevailing winds, and other unobserved covariates can all lead to unsuitable model fitting. We use a nonlinear model to avoid these problems and demonstrate its predictive power in relation to the dataset under study. Copyright © 2001 John Wiley & Sons, Ltd.
Wind speed prediction in a complex terrain
Denison, D. G. T. (author) / Dellaportas, P. (author) / Mallick, B. K. (author)
Environmetrics ; 12 ; 499-515
2001-09-01
17 pages
Article (Journal)
Electronic Resource
English
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