ARTÍCULO
TITULO

Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting

Daniel Silva Campos    
Yara de Souza Tadano    
Thiago Antonini Alves    
Hugo Valadares Siqueira    
Manoel Henrique de Nóbrega Marinho (Author)    

Resumen

Air pollution is a relevant issue studied worldwide, and its prediction is important for social and economic management. Linear multivariate regression models (LMR) and artificial neural networks (ANN) are widely applied to forecasting concentrations of pollutants. However, unorganized machines are scarcely used. The present investigation proposes the application of unorganized machines (echo state networks - ESN and extreme learning machines - ELM) to forecast hourly concentrations of particulate matter with the aerodynamic diameter up to 10 µm (PM10), carbon monoxide (CO), and ozone (O3) at the metropolitan region of Recife, Pernambuco, Brazil. The results were compared with multilayer perceptron neural network (MLP) and LMR. The prediction was made using or not meteorological variables (wind speed, temperature, and relative humidity) as input data. The results showed that the inclusion of these variables could increase the general performance of the models considering one step ahead forecasting horizons. Also, the ELM and the LMR achieved the best overall results.

 Artículos similares

       
 
Mashael Aldayel, Amira Kharrat and Abeer Al-Nafjan    
Individual choices and preferences are important factors that impact decision making. Artificial intelligence can predict decisions by objectively detecting individual choices and preferences using natural language processing, computer vision, and machin... ver más
Revista: Applied Sciences

 
Jöran Rixen, Nico Blass, Simon Lyra and Steffen Leonhardt    
Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not con... ver más
Revista: Algorithms

 
Weizhong Zeng, Ke Xu, Sihang Cheng, Lei Zhao and Kun Yang    
Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could p... ver más
Revista: Applied Sciences

 
Renteng Yuan, Shengxuan Ding and Chenzhu Wang    
Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using ... ver más
Revista: Infrastructures

 
Leian Zhang, Junwu Wang, Han Wu, Mengwei Wu, Jingyi Guo and Shengmin Wang    
Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing per... ver más
Revista: Applied Sciences