基于机器学习的预测不受限制的压力强度和污染物浸出性在疏污染沉积物中的污染物,用于土地回收项目
Aamir Khan Mastoi1, Saifal Hameed2, Mutahar Ali3
1Institute of Geotechnical and Underground Engineering, School of Civil Engineering & Hydraulics, Huazhong University of Science and Technology, 318 6th Building of the West, Wuhan, 430074, China. aamirkhan@quest.edu.pk.
概括
机器学习模型准确地预测沉积物强度和污染物浸出用于土地回收. XGBoost模型在决策树上表现出卓越的性能,提供了具有成本效益的解决方案.
科学领域:
- 地质技术工程 地质技术工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 评估用于土地回收的疏污染沉积物 (DCS) 是至关重要的,但传统上是昂贵和耗时的.
- 准确预测不受限制的压力强度 (UCS) 和污染物可泄漏性对于高效的项目规划至关重要.
研究的目的:
- 评估机器学习模型,特别是极端梯度增强 (XGBoost) 和决策树 (DT),用于预测DCS中的UCS和污染物可泄漏性.
- 提高预测准确度,减少对土地回收项目的广泛实验室测试的依赖.
主要方法:
- 开发和训练XGBoost和DT模型,使用165个数据样本和6个输入参数.
- 采用蒙特卡洛和K-fold交叉验证来进行可靠的模型评估和通用性评估.
- 进行了敏感性分析,以确定影响UCS和可性的主要参数.
主要成果:
- 在预测准确度方面,XGBoost模型显著优于DT模型,由更高的调整R平方 (Adj. R2) 和下根的平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 地面颗粒高炉渣 (GGBS) 含量被确定为影响UCS预测的关键因素.
- 发现度 (Z) 是可性预测的重要驱动因素.
结论:
- 机器学习,特别是XGBoost,为预测DCS的关键参数提供了一种强大而高效的方法.
- 这些精细的预测算法可以带来更具成本效益和精简的土地回收计划.
- 该研究强调了数据驱动方法在优化环境工程实践方面的潜力.
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