Air Quality Index Forecasting via Genetic AlgorithmBased Improved Extreme Learning Machine

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.