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SIMULATING SOIL WATER AND SOLUTE TRANSPORT IN A SOIL-WHEAT SYSTEM USING AN IMPROVED GENETIC ALGORITHM

机译:利用改进的遗传算法模拟土壤水分和溶质运输

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An improved genetic algorithm was applied and examined to optimize the weights of a neural network model for estimating root length density (RLD) distributions of winter wheat under salinity stress. Thereafter, soil water and solute transport with root - water - uptake in a soil-wheat system were simulated numerically, in which the estimated RLD distributions were incorporated. The results showed that the estimated RLD distributions of winter wheat using the neural network model combined with the improved genetic algorithm, as well as the simulated soil water content and salinity distributions, were comparably well with the experimental data. The method can serve in modeling flow and transport under salinity or saline water irrigated areas.
机译:应用了一种改进的遗传算法,并检查了盐度应力下冬小麦根长密度(RLD)分布的神经网络模型的重量。此后,数值模拟了土壤 - 小麦系统中的土壤水和溶质输送,在土壤 - 小麦系统中进行了数量模拟,其中含有估计的RLD分布。结果表明,使用神经网络模型与改进的遗传算法相结合的冬小麦估计的RLD分布,以及模拟的土壤水含量和盐度分布,与实验数据相比良好。该方法可用于在盐度或盐水灌溉区域下建模和运输。

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