Journal Article

A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting

Das, Abhishek, Bhanja, Samit, Metia, Santanu

2022

Overview

With the rapid increase of urbanization and industrialization, particulate matter (PM2.5) concentration has increased significantly. PM2.5 profile forecasting has become one of the critical research areas in environmental control and protection. The early detection of PM2.5 as a pollutant is vital because PM2.5 has a significant impact on human health than other pollutants. This paper proposes a deep neuro-fuzzy prediction system (DNFPS) by amalgamating the deep learning and the fuzzy time series algorithm to forecast the PM2.5 concentration. The proposed predictive model consists of three phases; a data preprocessing algorithm to generate a high-quality dataset, a denoising autoencoder using fully convolutional neural networks (FCNNs) to extract the features from the pollutant time series profile as well as reduce the dimension of the time series dataset, and the type-2 fuzzy time series forecasting (FTSF) method to forecast PM2.5 concentration. The butterfly optimization algorithm (BOA) is integrated with the type-2 FTSF method to improve the prediction accuracy of the proposed method. FTSF-BOA is implemented to fine-tune the length of type-2 fuzzy intervals. Experiments employing Sydney data sets to analyze the performance of DNFPS. DNFPS shows that the proposed model achieves an excellent performance than other standard baseline models. It has lower computational time (training time) than the other traditional baseline deep learning models.

• Publisher: Springer Berlin Heidelberg
• Subject: Electronics and Microelectronics / Engineering / Instrumentation / Mechanical Engineering / Nanotechnology / Technical Paper