دانلود مقاله ترجمه شده یک مدل هیبریدی جدید مبتنی بر الگوریتم بهینه سازی هریس هاوکس چند-هدفه برای پیش بینی روزانه ذرات PM2.5 و PM10

Abstract

High  levels  of  air  pollution  may  seriously  affect  people's  living  environment  and even endanger their lives. In order to reduce air pollution concentrations, and warn the public  before  the  occurrence  of  hazardous  air  pollutants,  it  is  urgent  to  design an accurate and reliable air pollutant forecasting model. However, most previous research have  many deficiencies,  such  as ignoring  the  importance  of  predictive stability,  and poor initial parameters and so on, which have significantly effect on the performance of air pollution prediction. Therefore, to address these issues, a novel hybrid model is proposed in this study. Specifically, a powerful data preprocessing techniques is applied to decompose the original time series into different modes from low-frequency to high-frequency. Next, a new multi-objective algorithm called MOHHO is first developed in this  study,  which  are  introduced  to tune the  parameters  of  ELM model  with high forecasting accuracy  and  stability  for  air pollution series prediction,  simultaneously. And the optimized ELM model is used to perform the time series prediction. Finally, a scientific  and  robust  evaluation  system  including  several  error criteria,  benchmark models, and several experiments using six air pollutant concentrations time series from three cities in China is designed to perform a compressive assessment for the presented hybrid forecasting model. Experimental results indicate that the proposed hybrid model can  guarantee  a  more  stable and higher  predictive  performance  compared  to  others, whose superior prediction ability may help to develop effective plans for air pollutant emissions and prevent health problems caused by air pollution.