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 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. 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. The thresholds, the weights, and the root mean square error are used to define the fitness function.
Finally, the least squares method is applied to compute the output weights of the model. Genetic algorithms can find the optimal solution in the
search space and gradually improve the performance of the model through an iterative optimization process. In order to verify the predictive
ability of GA-KELM, based on the collected basic data of long-term air quality forecast at a monitoring point in a city in China, the optimized
kernel extreme learning machine is applied to predict air quality ( SO_{2} ,NO_{2} , PM_{10} , CO , O_{3} , PM_{2.5} concentration and
AQI), with comparative experiments based CMAQ (Community Multiscale Air Quality), SVM (Support Vector Machines) and DBN-BP
(Deep Belief Networks with Back-Propagation). The results show that the proposed model trains faster and makes more accurate predictions.