Air Quality Index Forecasting via Genetic Algorithm - Based Improved Extreme Learning Machine

Abstract

Air quality has always been one of the most important environmental concerns for the general
public and society. Using machine learning algorithms for Air Quality Index (AQI) prediction
is helpful for the analysis of future air quality trends from a macro perspective. When
conventionally using a single machine learning model to predict air quality, it is challenging to
achieve a good prediction outcome under various AQI fluctuation trends. In order to effectively
address this problem, a genetic algorithm-based improved extreme learning machine (GA-
KELM) prediction method is enhanced. First, a kernel method is introduced to produce the
kernel matrix which replaces the output matrix of the hidden layer. To address the issue of the
conventional limit learning machine where the number of hidden nodes and the random
generation of thresholds and weights lead to the degradation of the network learning ability, a
genetic algorithm is then used to optimize the number of hidden nodes and layers of the kernel
limit learning machine.