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首页> 外文期刊>Journal of Hydroinformatics >Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content
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Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

机译：使用具有挑战性的土壤含水量估算来研究进化数据驱动技术的能力

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Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (ⅰ) Evolutionary Polynomial Regression and (ⅱ) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.
机译：土壤水分在全球能源和水文循环中都起着至关重要的作用。它影响不同的生态系统过程。土壤水分的时空变化增加了土壤的复杂性，这破坏了大多数当前测量方法的可靠性。在本文中，通过模拟土壤湿度对近地表大气条件的响应，挑战了两种有前途的进化数据驱动技术，即（ⅰ）进化多项式回归和（ⅱ）遗传规划。通过预测三个实验性土壤覆盖层的土壤水分响应的预测，证明了所提出模型的实用性，该土壤覆盖层用于修复受采矿业干扰的流域。结果表明，土壤水分响应的存储效应是主要的挑战性因素。可以使用累积输入比时滞输入更好地对其进行量化，这可以归因于土壤层持水能力的影响。随着土壤层厚度的增加，这种效果会增加。测试了三种不同的建模工具，以研究工具在数据驱动的建模中的效果。尽管在预测准确性方面有可喜的结果，但该研究表明仍需要采用多种数据驱动的建模技术和工具（建模环境）来获得可靠的预测。

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