地理加权机器学习模型的应用,用于预测整个矿场的土壤重金属度
Hyemin Jeong1, Younghun Lee1, Byeongwon Lee1
1Department of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
The Science of the total environment
|November 23, 2024
概括
地理加权机器学习模型 (GWMLMs) 通过考虑空间变化,显著改善了废弃矿山中的土壤重金属 (Cd,Pb) 预测. 这些模型比传统的环境管理方法提供更高的准确性.
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 机器学习
背景情况:
- 准确预测土壤重金属污染对于管理废弃矿场至关重要.
- 传统的机器学习模型与空间异质性作斗争,限制了预测准确性.
研究的目的:
- 评估地理加权机器学习模型 (GWMLMs),用于预测土壤中的 (Cd) 和 (Pb) 度.
- 将GWMLMs与废弃矿山环境中的传统机器学习模型 (CMLMs) 进行比较.
主要方法:
- 将两个GWMLM (地理加权随机森林,地理加权极端梯度增强) 与四个CMLM进行了比较.
- 使用了6个废弃矿场的土壤样本和地理/土壤输入变量.
- 采用了夏普利添加式解释,以确定关键的预测因素.
主要成果:
- 在Cd和Pb预测方面,GWMLMs的表现始终优于CMLMs.
- 与CMLMs相比,GWMLMs显示了较低的RMSE和MAE,以及较高的R2值.
- 从采矿场的高度和距离被确定为影响Cd和Pb度的关键因素.
结论:
- 通过结合空间异质性,GWMLMs有效地预测了土壤重金属的空间分布.
- 该研究强调了GWMLMs在改善矿业影响地区的环境管理方面的价值.
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