Environmental data mining and modeling based on machine learning algorithms and geostatistics

Détails

ID Serval
serval:BIB_F3AEFEC8F828
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Environmental data mining and modeling based on machine learning algorithms and geostatistics
Périodique
Environmental Modelling and Software
Auteur(s)
Kanevski M., Parkin R., Pozdnukhov A., Timonin V., Maignan M., Demyanov V., Canu S.
ISSN-L
1364-8152
Statut éditorial
Publié
Date de publication
2004
Peer-reviewed
Oui
Volume
19
Pages
845-855
Langue
anglais
Notes
1st Biennial Meeting of the International-Environmental-Modelling-and-Software-Society (iEMSs), Univ Lugano, Lugano, SWITZERLAND, JUL, 2002
Résumé
The paper presents some contemporary approaches to spatial environmental
data analysis. The main topics are concentrated on the decision-oriented
problems of environmental spatial data mining and modeling: valorization
and representativity of data with the help of exploratory data analysis,
spatial predictions, probabilistic and risk mapping, development and
application of conditional stochastic simulation models. The innovative
part of the paper presents integrated/hybrid model-machine learning (ML)
residuals sequential simulations-MLRSS. The models are based on
multilayer perceptron and support vector regression ML algorithms used
for modeling long-range spatial trends and sequential simulations of the
residuals. NIL algorithms deliver non-linear solution for the spatial
non-stationary problems, which are difficult for geostatistical
approach. Geostatistical tools (variography) are used to characterize
performance of ML algorithms, by analyzing quality and quantity of the
spatially structured information extracted from data with ML algorithms.
Sequential simulations provide efficient assessment of uncertainty and
spatial variability. Case study from the Chernobyl fallouts illustrates
the performance of the proposed model. It is shown that probability
mapping, provided by the combination of ML data driven and
geostatistical model based approaches, can be efficiently used in
decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.
Création de la notice
07/10/2012 15:53
Dernière modification de la notice
20/08/2019 16:20
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