气象变化和大气颗粒污染的预测预测
1Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam. wanyun.hong@ubd.edu.bn.
Scientific reports
|January 3, 2024
概括
准确预测空气中的颗粒物 (PM10) 对健康和气候至关重要. 这项研究使用气象数据和前一天的PM10度开发了预测模型,大大提高了预测准确度.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 数据科学数据科学数据科学
背景情况:
- 空气中的颗粒物 (PM) 构成已记录的健康风险,并影响气候变化.
- 了解和预测PM变异性对于缓解和适应战略至关重要.
- 大气中的PM10度受气象条件的影响,导致全球变暖.
研究的目的:
- 开发和验证大气PM10度的预测模型. 在文莱-穆阿拉.
- 评估气象参数和历史PM10数据对预测准确性的影响.
- 为了比较不同机器学习模型在预测PM10方面的性能.
主要方法:
- 开发和验证使用多线性回归 (MLR),随机森林 (RF),极端梯度增强 (XGBoost) 和人工神经网络 (ANN) 的PM10预测预测模型.
- 包括时间和气象参数,以及前一天的PM10度 (PM10,t-1),作为输入变量.
- 使用诸如根平均平方误差 (RMSE) 和R平方 (R2) 等指标评估模型性能.
主要成果:
- 纳入前一天的PM10度 (PM10,t-1) 显著提高了模型预测能力的57-92%.
- 使用PM10,t-1的MLR模型在捕捉季节性变异方面表现出最高的能力 (RMSE = 1.549μg/m3;R2 = 0.984).
- 带有PM10,t-1的RF和ANN模型分别为接下来的1,2和3天提供了准确的预测.
结论:
- 结合气象数据和历史PM10度的预测模型可以准确预测大气PM10水平.
- 包括前一天的PM10度是提高预测准确性的关键因素.
- 不同的建模方法 (MLR,RF,ANN) 为短期和中期PM10预测提供了不同的强度.
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