Journal Article Epublish 6 citations
PubMed: 40000767 DOI: 10.1038/s41598-025-91329-w
Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.
Sci Rep 15(1):6798
Authors: Qingchun Guo, Zhenfang He, Zhaosheng Wang
Affiliations
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China. Institute of Huanghe Studies, Liaocheng University, Liaocheng, 252000, China. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China. Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing, 100081, China.
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China. Institute of Huanghe Studies, Liaocheng University, Liaocheng, 252000, China.
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, National Ecosystem Science Data Center, Chinese Academy of Sciences, Beijing, 100101, China.
Abstract
Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major environmental problem in Liaocheng City. Long short-term memory (LSTM) and artificial neural network (ANN) models are established to predict ozone concentrations in Liaocheng City from 2014 to 2023. The results show a general improvement in the accuracy of the LSTM model compared to the ANN model. Compared to the ANN, the LSTM has an increase in determination coefficient (R), value from 0.6779 to 0.6939, a decrease in root mean square error (RMSE) value from 27.9895 μg/m to 27.2140 μg/m and a decrease in mean absolute error (MAE) value from 21.6919 μg/m to 20.8825 μg/m. The prediction accuracy of the LSTM is superior to the ANN in terms of R, RMSE, and MAE. In summary, LSTM is a promising technique for predicting ozone concentrations. Moreover, by leveraging historical data and LSTM enables accurate predictions of future ozone concentrations on a global scale. This model will open up new avenues for controlling and mitigating ozone pollution.
MeSH
Memory, Short-Term, Environmental Pollution, Natural Science Disciplines, Memory
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