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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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使用机器学习和超光谱遥感进行土壤元素预测的不确定性.

Xiumei Ma1, Jinlin Wang2, Kefa Zhou3

  • 1Department of Biology, Colorado State University, Fort Collins, CO 80523, USA.

Journal of hazardous materials
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概括

这一元分析揭示了最佳的超谱遥感和机器学习策略,用于绘制土壤潜在有毒元素 (PTE) 的地图. 推的FD-PCC-RF组合显著提高了环境风险评估的预测准确性.

关键词:
重金属元素是重金属的组成部分.超光谱遥感是一种超光谱遥感技术.机器学习 机器学习模型的准确性模型的准确性可能有毒的元素.

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科学领域:

  • 环境科学 环境科学
  • 地理空间分析的研究.
  • 机器学习应用 机器学习应用

背景情况:

  • 土壤中的潜在有毒元素 (PTE) 由于生物积累,存在持续的环境风险.
  • 与机器学习集成的超谱遥感为土壤PTE量化和绘制提供了一个有希望的途径.
  • 对这些综合方法的模型准确性的全面评估是有限的.

研究的目的:

  • 进行元分析,评估各种光谱转换,频段优化和机器学习 (ML) 技术的准确性,用于土壤PTE预测.
  • 确定最佳的预处理和建模策略,以提高土壤PTE绘图的准确性.
  • 为未来的土壤污染监测研究和实际应用提供建议.

主要方法:

  • 87项研究的元分析,包括97个地点和7个土壤元素.
  • 评估了42种光谱转换方法,16种频段优化方法和34种ML技术.
  • 统计分析以确定不同方法组合的预测性能 (R2).

主要成果:

  • 通过第一导数 (FD),第二导数 (SD),波形变换 (WT) 和连续移除 (CR) 的光谱变换实现了更高的精度.
  • 主要组件相关性 (PCC),主要组件分析 (PCA),专家知识 (EK) 和组合 (C_2) 频段优化方法提高了预测性能.
  • 随机森林 (RF),支持向量机器 (SVM),人工神经网络 (ANN),极端学习机器 (ELM) 和部分最小平方回归 (PLSR) 显示出高精度.
  • FD-PCC-RF组合产生了高的R2值 (例如,FD-RF的79.55%±13.26%).
  • 环境条件,采样设计和共变量影响模型准确性,但预处理优化至关重要.

结论:

  • 优化的预处理方法,特别是FD-PCC-RF策略,显著提高了土壤PTE预测的准确性.
  • 优先考虑科学优化的预处理对于最大限度地利用现场采样数据至关重要.
  • 这项研究强调了先进的预处理和模型集成对于有效的土壤PTE评估和管理的重要性.