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- W2131140282 abstract "59 The present research presents a review of advanced techniques fro the analysis and modelling of spatio-temporal environmental and pollution data: geostatistical simulations and risk mapping, machine learning algorithms ‐ artificial neural networks, statistical learning theory (support vector machines, support vector regression). The presentation is accompanied by the case studies form different fields: risk mapping of soil and water pollution, large scale mapping of radioactively contaminated territories, classification of hydro geological units and soil types, prediction of river levels and other geophysical signals, and others. Strictly speaking, there are several basic problems related to environmental spatiotemporal data: spatial predictions of continuous multivariate data or spatial multivariate regression, classification of spatially distributed categorical data, risk mapping or mapping of probabilities, and development of spatial Monte Carlo models which are able to generate the most complete and comprehensive information for real decision making process. A very important classical problem deals with monitoring network optimisation (design/redesign) or how to answer the basic question: where to go and what to measure? Traditionally some approaches to the mentioned above problems were developed by classical geostatistics. Despite of some achievements, they have limited applicability because they are based on some hypotheses ‐ linearity, second-order stationarity, etc. which could be rarely found in environmental studies dealing with many spatially nonstationary processes" @default.
- W2131140282 created "2016-06-24" @default.
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- W2131140282 date "2005-01-01" @default.
- W2131140282 modified "2023-09-27" @default.
- W2131140282 title "Review of advanced approaches for the analysis and modelling of spatio-temporal environmental and pollution data" @default.
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- W2131140282 hasPublicationYear "2005" @default.
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