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Нейросетевое моделирование загрязнения почв на территории горнопромышленного региона

Нейросетевое моделирование загрязнения почв на территории горнопромышленного региона

Abstract

Показано, что при комплексной обработке неоднородной и многоуровневой геохимической информации мониторинга почв идеальным условием является единая парадигма построения различных эмпирических моделей, решающих задачу прогнозирования геоэкологического состояния территории. Нейросетевое моделирование позволяет создавать адекватные математические модели в условиях ограниченности информации.

It’s shown that ideal condition is integrated paradigm of creating different empirical models, which solving problem of forecasting territorial environmental condition by complex processing heterogeneous and multilevel geochemical information of monitoring soils. Neu-ronet modeling allows creating adequate mathematical models at conditions of information limitation.

Keywords

ТЕРРИТОРИЯ, ПОЧВЕННЫЙ ПОКРОВ, ГЕОХИМИЧЕСКАЯ ИНФОРМАЦИЯ, ЗАГРЯЗНЕНИЕ, НЕЙРОСЕТЕВОЕ МОДЕЛИРОВАНИЕ, МОДЕЛЬ, ПРОГНОЗ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
bronze