MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

Feng Jiang,Yaqian Qiao,Tianhai Tian,Xuchu Jiang

doi:10.3390/atmos12010064

Abstract

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

Highlights

Empirical mode decomposition (EMD) is a kind of adaptive data analysis method utilized for the nonlinear and nonstationary signal that was proposed by Huang et al [24], and it can decompose a complex signal into several intrinsic mode functions that contain the local characteristics of the original signals at different time scales
Step 3: Prediction result aggregating. the prediction outcomes of intrinsic mode functions (IMFs) is obtained via summarizing the forecasting results of subseries decomposed by wavelet transform (WT), and we further summarize the results of IMFs to get the final prediction results
A hybrid approach of group teaching optimization algorithm (GTOA) and extreme learning machine (ELM) based on data preprocessing technology to predict the concentration of PM10 and PM2.5 is proposed

Summary

Introduction

The prediction of future events from the noisy and nonstationary time series data is a challenging problem. The forecasting of atmospheric pollution is one of the important problems in the analysis of noise and nonstationary time series. A precise short-term forecasting model should be developed to enhance the prediction performance of air-pollutant concentrations, which can give valuable support for decision-making of relevant departments, and can provide helpful information for air-quality monitoring systems. There is an increasing demand to develop data-driven algorithms based on hourly observations time series for making reliable short-term forecasting of particulate matter (PM) concentrations [9,10]. Due to the nonstationary, nonlinear and complex characteristic of PM2.5 and PM10, it is difficult to make a precise forecasting for it

Methods

Results

Conclusion