通过组合带选择方法改善土壤重金属反转:中国吉市的一个案例研究
Ping He1,2,3, Xianfeng Cheng4,5, Xingping Wen1
1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新的多算法方法,用于选择高光谱波段,以准确预测土壤 (Pb) 污染. 优化的WOA-GA-MI模型显著提高了预测准确性,为环境监测提供了有价值的工具.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 分析化学 分析化学
背景情况:
- 超光谱技术对于土壤重金属监测至关重要.
- 现有的频段选择方法往往有局限性,例如冗余性和依赖单个算法.
- 多算法带选择方法在土壤重金属评估中未得到充分利用.
研究的目的:
- 开发和评估一种多算法频段选择方法,用于快速预测土壤 (Pb) 污染.
- 解决超光谱数据中的带冗余问题,以改善重金属监测.
- 为了提高土壤Pb含量预测的准确性,使用优化的超光谱带组合.
主要方法:
- 应用单个算法:竞争性自适应重量化采样 (CARS),遗传算法 (GA),相互信息 (MI),连续预测算法 (SPA) 和鱼优化算法 (WOA).
- 开发了基于WOA的组合频段选择方法 (例如WOA-CARS,WOA-GA,WOA-MI,WOA-SPA).
- 实现了使用MI (例如,WOA-GA-MI,WOA-CARS-MI,WOA-SPA-MI) 的多级频段优化,以进行精细的Pb预测.
主要成果:
- 鱼优化算法 (WOA) 最初显示了最高的建模准确性.
- 该WOA-GA-MI模型实现了最佳性能,平均R2为0.75.
- 这种模型显示了比全频谱和更简单的模型显著的精度改进.
- 通过光谱响应分析确定了Pb含量逆转的22个基本常见频段.
结论:
- 拟议的多级组合频段选择方法显著提高了土壤Pb预测的准确性.
- 这种方法为优化环境监测的超光谱带选择提供了有价值的见解.
- 使用超光谱数据,为评估土壤重金属污染提供了坚实的科学基础.
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