一种新的方法,通过整合气象参数和生产强度来预测露天采矿中的尘埃度
Zhiming Wang1,2,3, Wei Zhou4,5,6, Izhar Mithal Jiskani7
1State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, China.
Environmental science and pollution research international
|October 20, 2023
概括
这项研究开发了一种机器学习模型,利用天气和生产数据预测每天的矿山灰尘污染. 该模型准确预测度,有助于绿色采矿举措.
科学领域:
- 环境科学 环境科学
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
背景情况:
- 矿山灰尘污染阻碍了可持续的采矿实践.
- 准确预测尘埃度对于环境管理至关重要.
研究的目的:
- 开发一种新的机器学习模型,用于预测露天矿山中每天的尘埃度.
- 整合天气预报和矿山生产强度数据,以提高预测准确度.
主要方法:
- 使用多种机器学习技术.
- 纳入天气预报数据 (例如,总列水蒸气) 和矿山生产强度 (岩石和煤炭生产) 作为输入.
- 使用皮尔森相关系数验证模型准确性.
主要成果:
- 开发的预测模型显示出高准确度,皮尔森相关系数超过0.87.
- PM2.5预测模型表现最好,其次是总悬浮粒子和PM10模型.
- 生产强度显著提高了模型性能,总柱水蒸气,岩石和煤炭生产是关键因素.
结论:
- 新的每日预报模型通过利用生产强度和气象数据准确预测矿山灰尘度.
- 该工具支持优化矿山设计,并使得人们能够为防止污染做出明智的决策.
- 它有助于最大限度地提高尘埃扩散和去除的自然条件,有助于气候智能采矿.
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