The research of the inductive method of long-term (forestalling to 0,5 year) prognosis of average decade air s temperature on the basis of principle of analogies was executed and it s sufficient was shown. The research of the offered approach was also conducted: in the base of spatial models without principle of analogies; in the polynomial harmonic base; the analysis of middle quality of the inductive prognostic method for cases of the analogue principle usage and without it

Canters, F.

de Jong, K.

Engelen, G.

FG Landschapskunde, Gis, Hydrologie

Karssenberg, D.J.

Landscape functioning, Geocomputation and Hydrology

Poelmans, L.

Seppelt, R., Voinov, A. A., Lange, S. & Bankamp, D.

Uljee, I.

van de Voorde, T.

van der Kwast, J.

A correct historic calibration of land-use models is important, because they

are more and more used by decision makers. Existing calibration methods, however,

do not sufficiently take into account uncertainties in input parameters. For that reason,

uncertainties that propagate through simulations of future land use are mostly unknown.

When uncertainties in model parameters can be estimated, Monte Carlo modelling can

be used to approximate the uncertainty in simulated land-use patterns. This study shows

that uncertainty information is not only indispensible for the interpretation of the output

of land-use models, but that this can also be used to improve the calibration of landuse

models by means of data assimilation. For this purpose the MOLAND model of

Dublin has been integrated in a Python-based data-assimilation framework. Calibration

of the land-use model is based on the comparison of spatial metrics derived from historic

remote sensing images and land-use simulation results. Remote sensing derived

probability density functions of class metrics are assimilated in the model at time steps

for which they are available. The particle filter algorithm only continues successful realizations

of the land-use model, thus reducing its uncertainty by removing unsuccesful

particles from the ensemble. In this way, only parameters sets used in the simulation that

match the patterns observed in the remote sensing imagery as quantified by the class

metrics, are used in the simulation of future land use. It is expected that the automatic

procedure results in an improved calibration of the land-use model. Furthermore, it provides

data on the uncertainty of the results, which is important for drawing conclusions

from simulations

Kwast, J. van der

Voorde, T. van de

Jong, K. de

NARCIS 

Uncertainty analysis and data-assimilation of remote sensing data for the calibration of cellular automata based land-use models

A correct historic calibration of land-use models is important, because they
are more and more used by decision makers. Existing calibration methods, however,
do not sufficiently take into account uncertainties in input parameters. For that reason,
uncertainties that propagate through simulations of future land use are mostly unknown.
When uncertainties in model parameters can be estimated, Monte Carlo modelling can
be used to approximate the uncertainty in simulated land-use patterns. This study shows
that uncertainty information is not only indispensible for the interpretation of the output
of land-use models, but that this can also be used to improve the calibration of landuse
models by means of data assimilation. For this purpose the MOLAND model of
Dublin has been integrated in a Python-based data-assimilation framework. Calibration
of the land-use model is based on the comparison of spatial metrics derived from historic
remote sensing images and land-use simulation results. Remote sensing derived
probability density functions of class metrics are assimilated in the model at time steps
for which they are available. The particle filter algorithm only continues successful realizations
of the land-use model, thus reducing its uncertainty by removing unsuccesful
particles from the ensemble. In this way, only parameters sets used in the simulation that
match the patterns observed in the remote sensing imagery as quantified by the class
metrics, are used in the simulation of future land use. It is expected that the automatic
procedure results in an improved calibration of the land-use model. Furthermore, it provides
data on the uncertainty of the results, which is important for drawing conclusions
from simulations

Utrecht University Repository

Зубов, Д.А.

Григоренко, М.С.

Наукова електронна бібліотека періодичних видань НАН України (Vernadsky National Library of Ukraine)