• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou China
【2h】

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou China

机译：基于熵权的综合预测模型在广州PM2.5浓度趋势中的应用研究

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.
机译：对于雾霾问题，PM2.5是中国雾霾污染的主要影响因素。本研究基于数学模型和模拟从定性的角度分析了PM2.5浓度的变化趋势。基于组合预测思想，开发了综合预测模型（CFM）。使用自回归综合移动平均模型（ARIMA），人工神经网络（ANNs）模型和指数平滑方法（ESM）预测PM2.5浓度的时间序列数据。综合预测模型的结果是根据熵权重法将三种方法的权重结合起来获得的。基于综合预测模型对广州市PM2.5浓度趋势进行了定量预测。将结果与三个单一模型的结果进行比较，并预测了未来10天的PM2.5浓度值。综合预测模型平衡了每种预测方法的偏差，具有较好的适用性。它为空气质量预测领域拓展了一种新的预测方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号