Authors :
Y V Nagesh Meesala; Avanapu Uma Mahesh; Abhiram Reddy Bellana; Bichukathula Obulesu
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yc5kpv4n
Scribd :
https://tinyurl.com/3b5zheb2
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2035
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Air quality prediction plays a vital role in
safeguarding public health and guiding environmental
policy. Traditional single-model approaches often
struggle to accurately forecast air quality fluctuations. In
response, this study introduces a robust prediction
system leveraging advanced machine learning
techniques. We present a comparative analysis of several
models including Support Vector Regression (SVR),
Genetic Algorithm-Enhanced Extreme Learning
Machine (GA-KELM), and Deep Belief Network with
Back-Propagation (DBN-BP). Additionally, we propose
the integration of Bidirectional Long Short-Term
Memory (BiLSTM), a deep learning architecture, to
further enhance prediction accuracy. Through
comprehensive experimentation and evaluation, we
demonstrate that BiLSTM outperforms existing models,
exhibiting lower Root Mean Square Error (RMSE) and
Mean Squared Error (MSE) values. Furthermore, by
incorporating GA-KELM, we optimize the performance
of BiLSTM, enhancing its predictive capabilities even
further. The proposed hybrid model not only offers
improved accuracy in air quality forecasting but also
contributes to informed decision-making for pollution
control strategies and public health interventions. This
research underscores the significance of exploring
innovative techniques to address pressing environmental
challenges and underscores the potential of machine
learning in advancing air quality management.
Keywords :
Time Series, Air Quality Forecasting, Machine Learning, Extreme Learning Machine, Genetic Algorithm.
Air quality prediction plays a vital role in
safeguarding public health and guiding environmental
policy. Traditional single-model approaches often
struggle to accurately forecast air quality fluctuations. In
response, this study introduces a robust prediction
system leveraging advanced machine learning
techniques. We present a comparative analysis of several
models including Support Vector Regression (SVR),
Genetic Algorithm-Enhanced Extreme Learning
Machine (GA-KELM), and Deep Belief Network with
Back-Propagation (DBN-BP). Additionally, we propose
the integration of Bidirectional Long Short-Term
Memory (BiLSTM), a deep learning architecture, to
further enhance prediction accuracy. Through
comprehensive experimentation and evaluation, we
demonstrate that BiLSTM outperforms existing models,
exhibiting lower Root Mean Square Error (RMSE) and
Mean Squared Error (MSE) values. Furthermore, by
incorporating GA-KELM, we optimize the performance
of BiLSTM, enhancing its predictive capabilities even
further. The proposed hybrid model not only offers
improved accuracy in air quality forecasting but also
contributes to informed decision-making for pollution
control strategies and public health interventions. This
research underscores the significance of exploring
innovative techniques to address pressing environmental
challenges and underscores the potential of machine
learning in advancing air quality management.
Keywords :
Time Series, Air Quality Forecasting, Machine Learning, Extreme Learning Machine, Genetic Algorithm.
