机器学习模型的性能分析用于Gorakhpur市的AQI预测:一个关键的研究研究
Mandvi1, Prabhat Kumar Patel1, Hrishikesh Kumar Singh2
1Department of Civil Engineering, Institute of Engineering and Technology, Uttar Pradesh, Lucknow, 226021, India.
Environmental monitoring and assessment
|September 12, 2024
概括
本研究将XGBoost和Lasso回归进行比较,用于预测空气质量指数 (AQI),使用PM2.5和NO2等污染物. 在预测印度戈拉克普尔的AQI时,XGBoost表现出卓越的准确性和较低的错误率.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 空气污染和气候变化对环境过程产生重大影响.
- 空气质量指数 (AQI) 对于管理空气污染影响至关重要.
- 预测AQI对于城市环境健康管理至关重要.
研究的目的:
- 使用机器学习模型预测空气质量指数 (AQI).
- 为AQI预测进行XGBoost和拉索回归的比较分析.
- 用PM2.5,PM10,SO2和NO2等污染物的各种统计指标来评估模型性能.
主要方法:
- 利用机器学习模型,特别是XGBoost和拉索回归.
- 专注于基于污染物度 (PM10,PM2.5,SO2,NO2) 的 AQI 预测.
- 采用统计验证指标,包括R平方,MAE,MSE,RMSE,T测试和p值.
主要成果:
- 与拉索回归 (0.9218) 相比,XGBoost实现了更高的R平方值 (0.9985).
- XGBoost的平均绝对误差 (MAE),平均平方误差 (MSE) 和根平均平方误差 (RMSE) 显著降低.
- 统计测试证实了显著的性能差异,有利于XGBoost而不是拉索回归.
结论:
- 与拉索回归相比,XGBoost是一个更准确,更可靠的模型来预测AQI.
- 该研究强调了机器学习在环境监测和预测方面的有效性.
- 研究结果为印度戈拉克普尔的城市空气质量管理策略提供了宝贵的见解.
相关概念视频
01:18Steps in Outbreak Investigation
114
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:
114
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
33
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...
33
01:03Prediction Intervals
2.2K
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.
2.2K
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
103
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
103
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
01:11Regression Analysis
5.7K
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:
5.7K