首页|Deep-learning architecture for PM2.5 concentration prediction:A review
Deep-learning architecture for PM2.5 concentration prediction:A review
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Accurately predicting the concentration of fine particulate matter(PM2.5)is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL)models for forecasting PM2.5 concentrations.Nonetheless,there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models.Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness.We categorized PM2.5 DL methodologies into seven types based on per-formance and application conditions,including four types of DL-based models and three types of hybrid learning models.Our research indicates that established deep learning architectures are commonly used and respected for their efficiency.However,many of these models often fall short in terms of innovation and interpretability.Conversely,models hybrid with traditional approaches,like deterministic and sta-tistical models,exhibit high interpretability but compromise on accuracy and speed.Besides,hybrid DL models,representing the pinnacle of innovation among the studied models,encounter issues with interpretability.We introduce a novel three-dimensional evaluation framework,i.e.,Dataset-Method-Experiment Standard(DMES)to unify and standardize the evaluation for PM2.5 predictions using DL models.This review provides a framework for future evaluations of DL-based models,which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.
PM2.5 concentration predictionDeep-learning based modelBibliometrics analysisEvaluation framework
Shiyun Zhou、Wei Wang、Long Zhu、Qi Qiao、Yulin Kang
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Institute of Environmental Information,Chinese Research Academy of Environmental Sciences,Beijing 100012,China
School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China
College of Water Sciences,Beijing Normal University,Beijing 100875,China
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