An Efficient Joint Evolutionary Algorithm-based Neural Network Model for Air Quality Prediction

An Efficient Joint Evolutionary Algorithm-based Neural Network Model for Air Quality Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Efficient Joint Evolutionary Algorithm-based Neural Network Model for Air Quality Prediction Peiyang Wei, Mingsheng Shang, Xi Chen, Yuyan Wang, Jianhong Gan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8076806/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract The Air Quality Index (AQI) prediction is an important tool for assessing and managing air pollution, with broad application value. In recent years, deep learning methods have compensated for the deficiencies in prediction accuracy of traditional AQI prediction methods due to their advantages in handling nonlinear data and feature learning. However, existing deep learning-based AQI prediction methods have not adequately considered temporal logic during the training process and lack differentiated handling of different types of features. Furthermore, the hyperparameter tuning process of network models is complex and inefficient. To address these issues, this paper presents a joint optimization network model (JONM) based on adaptive weights to achieve more accurate air quality prediction. The main innovations include four aspects: a) a novel spliced multi-step prediction method is introduced, which segments the input sequence for prediction, ensuring non-iterative accumulation of errors while fully considering the temporal relationships of the predicted sequence, thereby resolving the error iteration problem in traditional multi-step predictions, b) the input feature data is processed differentially by classifying the input features into primary features (AQI) and secondary features, allowing different features to flow through neural network layers of varying depths. This ensures that while the model learns secondary information to assist in predictions, AQI prediction remains the primary task, avoiding interference between primary and secondary tasks, c) a joint evolutionary algorithm is constructed using eight evolutionary algorithms with different mutation strategies, guiding the model's prediction results by weighted summation of the hyperparameter groups optimized by each evolutionary algorithm, d) particle swarm optimization is utilized to optimize the joint weights of the prediction results from the eight evolutionary algorithms, thereby enhancing computational efficiency. Experiments and evaluations on real AQI datasets indicate that the proposed JONM outperforms the benchmark model, validating the effectiveness of the joint strategy and the accuracy of the prediction results. AQI prediction Adaptive hyperparameter optimization Time series forecasting model Evolutionary algorithm Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 28 Dec, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 10 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8076806","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614588699,"identity":"55fb9f68-fc8e-42b0-bf71-457d57a70b6b","order_by":0,"name":"Peiyang 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In recent years, deep learning methods have compensated for the deficiencies in prediction accuracy of traditional AQI prediction methods due to their advantages in handling nonlinear data and feature learning. However, existing deep learning-based AQI prediction methods have not adequately considered temporal logic during the training process and lack differentiated handling of different types of features. Furthermore, the hyperparameter tuning process of network models is complex and inefficient. To address these issues, this paper presents a joint optimization network model (JONM) based on adaptive weights to achieve more accurate air quality prediction. The main innovations include four aspects: a) a novel spliced multi-step prediction method is introduced, which segments the input sequence for prediction, ensuring non-iterative accumulation of errors while fully considering the temporal relationships of the predicted sequence, thereby resolving the error iteration problem in traditional multi-step predictions, b) the input feature data is processed differentially by classifying the input features into primary features (AQI) and secondary features, allowing different features to flow through neural network layers of varying depths. This ensures that while the model learns secondary information to assist in predictions, AQI prediction remains the primary task, avoiding interference between primary and secondary tasks, c) a joint evolutionary algorithm is constructed using eight evolutionary algorithms with different mutation strategies, guiding the model's prediction results by weighted summation of the hyperparameter groups optimized by each evolutionary algorithm, d) particle swarm optimization is utilized to optimize the joint weights of the prediction results from the eight evolutionary algorithms, thereby enhancing computational efficiency. 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