Optimizing Long Short-Term Memory Network for Air Pollution Prediction Using a Novel Binary Chimp Optimization Algorithm

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it...

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Vydané v:Electronics (Basel) Ročník 12; číslo 18; s. 3985
Hlavní autori: Baniasadi, Sahba, Salehi, Reza, Soltani, Sepehr, Martín, Diego, Pourmand, Parmida, Ghafourian, Ehsan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2023
Predmet:
Accuracy
Air pollution
Air quality
Algorithms
Atmospheric models
Collaboration
Computer architecture
Distribution
Environmental aspects
Environmental protection
Forecasting
Machine learning
Mathematical models
Mathematical optimization
Monkeys & apes
Optimization
Optimization algorithms
Outdoor air quality
Particles
Performance evaluation
Pollutants
Prediction models
Root-mean-square errors
United States
ISSN:2079-9292, 2079-9292
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Shrnutí:Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it furnishes indispensable information for epidemiological studies concentrating on PM2.5 exposure. In recent years, predictive models based on deep learning (DL) have offered promise in improving the accuracy and efficiency of air quality forecasts when compared to other approaches. Long short-term memory (LSTM) networks have proven to be effective in time series forecasting tasks, including air pollution prediction. However, optimizing LSTM models for enhanced accuracy and efficiency remains an ongoing research area. In this paper, we propose a novel approach that integrates the novel binary chimp optimization algorithm (BChOA) with LSTM networks to optimize air pollution prediction models. The proposed BChOA, inspired by the social behavior of chimpanzees, provides a powerful optimization technique to fine-tune the LSTM architecture and optimize its parameters. The evaluation of the results is performed using cross-validation methods such as the coefficient of determination (R2), accuracy, the root mean square error (RMSE), and receiver operating characteristic (ROC) curve. Additionally, the performance of the BChOA-LSTM model is compared against eight DL architectures. Experimental evaluations using real-world air pollution data demonstrate the superior performance of the proposed BChOA-based LSTM model compared to traditional LSTM models and other optimization algorithms. The BChOA-LSTM model achieved the highest accuracy of 96.41% on the validation datasets, making it the most successful approach. The results show that the BChOA-LSTM architecture performs better than the other architectures in terms of the  R2 convergence curve, RMSE, and accuracy.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12183985