在印度加尔各答大都市的COVID-19封锁期间使用MLR和ANN模型预测PM2.5的度
Biswajit Bera1, Sumana Bhattacharjee2, Nairita Sengupta3
1Department of Geography, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Purulia Sainik School, 723104, India.
概括
人工神经网络 (ANN) 模型准确地预测了加尔各答的颗粒物 (PM2.5) 空气污染. 该研究发现,在城市空气质量管理中,ANN模型优于多重线性回归.
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:
- 卡尔卡塔是一个人口密集的城市,面临着严重的空气污染挑战,位列世界上污染最严重的城市之一.
- 由于COVID-19流行病的封锁,加尔各答的空气污染水平显著降低.
研究的目的:
- 使用多线性回归 (MLR) 和人工神经网络 (ANN) 模型预测加尔各答的PM2.5度.
- 为了比较PM2.5度的MLR和ANN模型的预测准确度.
主要方法:
- 从西孟加拉邦污染控制局收集了PM2.5数据.
- 从全球气象网站收集每日气象数据.
- 开发和评估了MLR和ANN模型来预测PM2.5水平.
主要成果:
- 人工神经网络 (ANN) 模型与多重线性回归 (MLR) 模型相比,显示出更高的精度和准确性.
- 在特定指标 (例如,训练中的R2为0.91,测试中的R2为0.69) 上,ANN模型取得了强的表现.
- ANN模型显示根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 比MLR更低.
结论:
- 人工神经网络 (ANN) 模型在预测加尔各答PM2.5度方面更有效.
- 开发的ANN模型可以成为实施城市空气质量管理计划的宝贵工具.
- 准确的PM2.5预测对于减轻人口密集的城市地区的空气污染至关重要.
更多相关视频
相关概念视频
Steps in Outbreak Investigation
152
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:
152
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Prediction 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
Mechanistic Models: Compartment Models in Individual and Population Analysis
64
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...
64


