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THE PROBLEM OF THE ESTIMATION OF THE INDUSTRIAL SOIL POLLUTION EXTENT

机译:工业土壤污染程度估算问题

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Significant spatial variability of the accumulation of pollutants in soils can make problems in the determination of the borders defining a zone where pollution, according to the applied legal requirements, is excessive. Particular difficulty is caused by a short-distance variability, disturbing the regularity in a spatial distribution of pollution around the source of emission. The paper presents an alternative, compared to traditional interpolation methods, algorithms based on the optimization andthe application non-linear neural networks called mixture density network MDN and feature space mapping network FSM. The benefit from the application of this approach is more information referring to the distribution of pollution. This approach allows the estimation of the local variance of the accumulation of pollutants and approximate local distribution. This allows greater extent of taking into account the uncertainty connected with the spatial variability of soil pollution.
机译:根据适用的法律要求,土壤中污染物积累的显着空间变异性可能会在确定边界区域的边界确定方面造成问题。特殊的困难是由短距离的可变性引起的,扰乱了排放源周围污染的空间分布规律。与传统的插值方法相比,本文提出了一种基于优化的算法和应用非线性神经网络的方法,称为混合密度网络MDN和特征空间映射网络FSM。应用此方法的好处是可以得到更多有关污染分布的信息。这种方法可以估算污染物累积的局部方差和近似的局部分布。这样可以更大程度地考虑与土壤污染的空间变异性相关的不确定性。

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