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Updated: Jun 6, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
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机器学习和回归在石矿在康复过程中的排水管理中
Aline Gonçalves Spletozer1,2, Elpidio Inacio Fernandes Filho3, Angeline Martini4
1Department of Forest Engineering, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil. alinespletozer_afmt@hotmail.com.
Environmental science and pollution research international
|December 2, 2024
概括
使用线性回归和人工智能模型预测石矿区的每月排水量,有助于水资源管理. 像Cubist和Random Forest这样的模型显示出强大的预测能力,可以预测康复地区的下水.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 数据科学数据科学数据科学
背景情况:
- 在石矿区有效的水资源管理需要准确的每月排水预测.
- 康复阶段显著影响水文过程,需要定制的预测模型.
研究的目的:
- 开发和评估结合的线性回归和人工智能模型,用于预测石开采区每月的排水情况.
- 确定影响恢复区下水预测的关键水文变量.
主要方法:
- 使用相关性值的变量选择,逐步选择和递归特征消除 (RFE).
- 使用年度数据周期对五个机器学习模型 (LM,KNN,SVM,RF,Cubist) 的培训和验证.
- 使用降水,持续时间,强度和事件计数作为主要输入变量.
主要成果:
- 线性回归模型在预测表面流水方面表现令人满意.
- 立体主义和随机森林模型是大多数康复年的主要预测因素,R2值从0.52到0.88.
- 没有一个单一的模型始终优于其他模型;建议每年选择一个模型.
结论:
- 线性回归方程和机器学习算法是石矿山每月排水预测的有效工具.
- 该研究为优化水文规划在地表石采矿作业的基础.
- 建议采用年度,特定年份的模型来准确预测康复地区的下水流量.
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