一个基于模糊时间序列和空气质量指数错误分布特征的智能间隔预测系统
Hufang Yang1, Yuyang Gao2, Fusen Zhao1
1School of Economics, Nanjing University of Posts and Telecommunications, Nanjing, China.
Environmental research
|March 3, 2024
概括
本研究引入了一种创新的空气质量指数 (AQI) 预测系统. 它有效地解决了数据模糊性和不确定性,改善了环境保护措施.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 预测 预测 预测 预测
背景情况:
- 空气质量指数 (AQI) 预测对于环境治理至关重要.
- 传统方法与数据模糊性和预测不确定性作斗争.
- 现有的方法往往为空气质量预测带来不满意的结果.
研究的目的:
- 开发空气质量指数 (AQI) 的创新预测系统.
- 解决处理数据模糊性和不确定性的传统方法的局限性.
- 提高空气质量预测的准确性和可靠性.
主要方法:
- 在核心预测过程中使用模糊时间序列.
- 采用完整的集体实证模式分解来进行数据无声化.
- 应用内核模糊c-means (KFCM) 集群用于间隔分区.
- 开发了一个基于错误分布的间隔预测方法来量化不确定性.
主要成果:
- 开发的系统在空气质量指数预测方面表现卓越.
- 整合KFCM和模糊时间序列有效地处理数据模糊性.
- 该方法通过间隔预测成功量化了预测不确定性.
- 实验模拟验证了系统的有效性和实际适用性.
结论:
- 创新的预测系统显著提高了空气质量指数预测准确度.
- 该系统为环境管理和预警系统提供了强大的解决方案.
- 这种方法为了解和减轻空气污染影响提供了一个有希望的工具.
更多相关视频
10:36Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Published on: November 3, 2023
1.5K
08:23Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
Published on: March 9, 2018
8.9K
相关概念视频
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
01:04Random Error
882
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...
882
00:55Interval Level of Measurement
14.9K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
14.9K
00:54Uncertainty: Confidence Intervals
4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
01:10Propagation of Uncertainty from Systematic Error
518
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
518
01:21Confidence Intervals
6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
A...
6.2K