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首页> 外文会议>International Conference on Computer Science and Information Technologies >A Method for Reconstruction of Unmeasured Data on Seasonal Changes of Microorganisms Quantity in Heavy Metal Polluted Soil
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A Method for Reconstruction of Unmeasured Data on Seasonal Changes of Microorganisms Quantity in Heavy Metal Polluted Soil

机译：一种重建未测量数据的方法，了解重金属污染土壤中微生物量的季节变化

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The article presents results of application of the hybrid combinatorial-genetic algorithm COMBI-GA to building models simulating the dependence of quantity of microorganisms in soil on the meteorological conditions and concentration of a heavy metal in an experimental plot. The models built on the rarely measured data during the vegetation seasons are used then for reconstructing the unmeasured decade data on seasonal changes of microorganisms quantity in the soil of a polluted plot during the whole season taking into account the complete support series of the decade meteorological data. This method is demonstrated on the results of modelling amylolytic microorganisms quantity dependence on measured weather factors and concentration of copper in the soil of experimental plots. Meteorological data included the humidity and temperature of air of the current and previous decades. Linear and nonlinear models of changing the microorganisms quantity in control and polluted plots are build based on the rarely measured data during the vegetation seasons. Nonlinear models are used for reconstructing the unmeasured decade data taking into account the complete support series of the decade weather data. Such a methodology can reduce in the future the cost of expensive and time-consuming experiments. A generalized model of amylolytic microorganisms quantity dependence on copper concentration and weather factors is created for predicting critical ecological situations.
机译：本文介绍了杂交组合 - 遗传算法Combi-Ga的应用结果模拟模拟微生物在土壤中微生物量依赖性的模型，在实验图中的气象条件和重金属中的浓度。在植被季节期间建立在很少测量的数据上的模型，然后用于在整个季节期间重建在整个赛季的整个赛季在整个赛季的污染地块的土壤中微生物数量的季节性变化的季节性变化的模型进行了考虑到十年气象数据的完整支持系列。该方法是对模拟淀粉溶解微生物量依赖性对实验图中的土壤中铜的浓度的结果的结果。气象数据包括当前和上几十年的空气湿度和温度。改变控制和污染图中微生物量的线性和非线性模型基于植被季节期间的很少测量数据来构建。非线性模型用于重建未测量的十年数据，考虑到十年天气数据的完整支持系列。这种方法可以减少未来昂贵且耗时的实验的成本。为预测关键生态情况，产生了淀粉溶解微生物量依赖性依赖性的广义模型。

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