Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection

Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection

Abstract In most industrial and urban regions, monitoring and safeguarding the air’s purity is considered one of the most crucial tasks for government agencies. In numerous industrial and urban locations, preserving and tracking the condition of the air has become the primary concern. However, imple...

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Bibliographic Details
Main Authors: R. Sudha, Ajith Damodaran, Gunaselvi Manohar
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Air quality prediction
Health assessment
Optimal weighted feature selection
Real-time air data
Adaptive residual Bi-LSTM network with pyramid dilation
Improved gannet optimization algorithm
Online Access:https://doi.org/10.1038/s41598-025-14668-8
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