使用混合随机森林和ARIMA模型对空气质量指数进行可解释的预测
Anuradha Yenkikar1,2, Ved Prakash Mishra1, Manish Bali1
1School of Engineering, Amity University Dubai Campus, Dubai, 25314, United Arab Emirates.
MethodsX
|August 8, 2025
概括
本研究引入了一种混合模型,将随机森林回归和ARIMA结合起来,用于准确的空气质量指数 (AQI) 预测. 可解释的框架提高了预测的准确性,并有助于公共卫生决策.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 准确的空气质量指数 (AQI) 预测对于公共卫生和环境政策至关重要.
- 现有的模型可能缺乏可解释性或与复杂的污染物相互作用作斗争.
研究的目的:
- 开发一个混合预测框架,整合随机森林回归 (RFR) 和自行回归集成移动平均线 (ARIMA),以改进AQI预测.
- 用SHAP分析确保模型的透明度和可解释性.
- 用多年数据和强大的交叉验证来验证模型的性能和通用性.
主要方法:
- 一个双阶段混合模型,将非线性污染物关系的RFR和剩余时间模式的ARIMA结合起来.
- 关于印度多年AQI数据的培训和评估.
- 扩大窗口交叉验证以获得时间完整性和SHAP分析以获得可解释性.
主要成果:
- 混合模型实现了508.46的平均平方误差 (MSE) 和0.94的R2得分,表现优于基线模型.
- SHAP分析确定了关键的污染物影响 (PM2.5,NO2,SO2).
- 隆格盒诊断和不确定性波段证实了模型的充分性.
结论:
- 拟议的混合框架为AQI预测提供了可复制,可解释和高效的解决方案.
- 这种方法适合在资源有限的城市环境中部署.
- 整合RFR,ARIMA和SHAP可以提高空气质量管理的预测准确性和可解释性.
相关概念视频
01:18Steps in Outbreak Investigation
204
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
204
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:11Regression Analysis
6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
01:19Survival Tree
160
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
160
01:04Random Error
1.6K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
1.6K
01:25Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K