Accurate Air-Quality Prediction Using Genetic-Optimized Gated-Recurrent-Unit Architecture

Abstract

Air pollution is becoming a serious concern with the development of society and urban expansion, and predicting air quality is the most pressing problem for human beings. Recently, more and more machine-learning-based methods are being used to solve the air-quality-prediction problem, and gated recurrent units (GRUs) are a representative method because of their advantage for processing time-series data. However, in the same air-quality-prediction task, different researchers have always designed different structures of the GRU due to their different experiences. Data-adaptively designing a GRU structure has thus become a problem. In this paper, we propose an adaptive GRU to address this problem, and the adaptive GRU structures are determined by the dataset, which mainly contributes with three steps. Firstly, an encoding method for the GRU structure is proposed for representing the network structure in a fixed-length binary string; secondly, we define the reciprocal of the sum of the loss of each individual as the fitness function for the iteration computation; thirdly, the genetic algorithm is used for computing the data-adaptive GRU network structure, which can enhance the air-quality-prediction result. The experiment results from three real datasets in Xi’an show that the proposed method achieves better effectiveness in RMSE and SAMPE than the existing LSTM-, SVM-, and RNN-based methods.