使用机器学习和地理信息系统数据预测土壤表面的流密度
Sakhaiaan Gavriliev1, Tatiana Petrova2, Petr Miklyaev3
1Radiochemistry Department, Faculty of Chemistry, Lomonosov Moscow State University, Russian Federation; Sergeev Institute of Environmental Geoscience, RAS, Moscow, Russian Federation.
The Science of the total environment
|August 17, 2023
概括
这项研究比较了机器学习算法来绘制莫斯科的流密度图. 随机森林 (RF) 和人工神经网络 (ANN) 显示出最佳表现,RF 非常适合划分,ANN 则适合平均值预测.
科学领域:
- 环境科学 环境科学
- 地质物理学 地质物理学
- 数据科学数据科学数据科学
背景情况:
- 流密度 (RFD) 映射对于了解暴露风险至关重要.
- 机器学习为环境因素的空间建模提供先进的工具.
研究的目的:
- 评估和比较用于构建莫斯科RFD地图的机器学习算法.
- 确定用于线划分和预测的最有效的算法.
主要方法:
- 采用人工神经网络 (ANN),随机森林 (RF) 和多变量自适应回归线 (MARS).
- 使用的预测因素包括地质数据,海拔,226Ra含量,环境剂量等效率 (ADER) 和接近地质特征.
- 从756个地点使用广泛的RFD测量训练模型.
主要成果:
- ANN和RF算法生成了高质量的RFD地图,具有强大的相关性和低误差.
- 与ANN和RF相比,MARS的表现较低.
- 射频预测更保守,而ANN预测显示了更现实的价值分布.
结论:
- 对于线划分的目的,RF是优越的.
- 对于预测平均RFD值来说,ANN更适合使用.
- 通过生成的地图,确定了影响莫斯科运输的关键因素.
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