通过时间序列模型预测环境PM2.5度,在中国安省
Ahmad Hasnain1, Muhammad Zaffar Hashmi2,3,4, Sohaib Khan5
1Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China.
Environmental monitoring and assessment
|April 30, 2024
概括
随机森林 (RF) 模型准确预测细颗粒物 (PM2.5) 污染,性能优于Prophet和ARIMA模型. 这一进步有助于预测PM2.5水平,以更好地管理环境和公共卫生.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 工业化和经济增长在全球范围内加剧了颗粒物 (PM2.5) 污染.
- 高度的PM2.5对公众健康和社会发展构成重大风险.
- 准确预测PM2.5对于有效的环境管理和政策实施至关重要.
研究的目的:
- 用各种时间序列模型预测中国安省环境PM2.5度.
- 为了比较随机森林 (RF),先知预测模型 (PFM) 和自行回归集成移动平均线 (ARIMA) 的预测性能.
- 确定该地区PM2.5预测最有效的模型.
主要方法:
- 使用时间序列分析预测PM2.5水平.
- 使用了三个模型:随机森林 (RF),先知预测模型 (PFM) 和自行回归集成移动平均线 (ARIMA).
- 通过交叉验证,使用R2,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 评估了模型性能.
主要成果:
- 射频模型表现出优异的性能,其中R2=0.83,RMSE=10.39μg/m3,MAE=6.83μg/m3.
- PFM取得了适度的结果 (R2=0.71,RMSE=13.90 μg/m3,MAE=9.05 μg/m3) 没有任何影响.
- 在ARIMA中,预测准确度最差 (R2=0.64,RMSE=15.85μg/m3,MAE=10.59μg/m3).
结论:
- 随机森林模型是预测PM2.5度的最有效方法.
- 这些发现表明,RF对于PM2.5预测在其他地理区域的适用性.
- 使用射频进行准确的PM2.5预测可以支持环境保护和公共卫生战略.
更多相关视频
05:45Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
10.6K
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
2.2K
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:23Linear Approximations
For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...