« Previous AbstractOptical bioelectronic nose of outstanding sensitivity and selectivity toward volatile organic compounds implemented with genetically engineered bacteriophage: Integrated study of multi-scale computational prediction and experimental validation    Next Abstract"Right stereoisomers for sex pheromone components of the apple leafminer, Lyonetia prunifoliella, in Korea" »

Int J Environ Res Public Health

Title:		A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network
Author(s):		Park J; Chang S;
Address:		"Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Deajeon 34141, Korea"
Journal Title:		Int J Environ Res Public Health
Year:		2021
Volume:		20210624
Issue:		13
Page Number:		-
DOI:		10.3390/ijerph18136801
ISSN/ISBN:		1660-4601 (Electronic) 1661-7827 (Print) 1660-4601 (Linking)
Abstract:		"Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively"
Keywords:		"*Air Pollutants/analysis *Air Pollution/analysis Environmental Monitoring Humans Memory, Short-Term Neural Networks, Computer Particulate Matter/analysis air pollution artificial neural network fine particulate matter long short-term memory prediction mod;"
Notes:		"MedlinePark, Junbeom Chang, Seongju eng Research Support, Non-U.S. Gov't Switzerland 2021/07/03 Int J Environ Res Public Health. 2021 Jun 24; 18(13):6801. doi: 10.3390/ijerph18136801"