Hga-lstm: LSTM architecture and hyperparameter search by hybrid GA for air pollution prediction

Abstract

Air pollution prediction is a process of predicting the levels of air pollutants in a specific area over a given period. Since LSTM (Long Short-Term Memory) networks are particularly effective in capturing long-term dependencies and patterns in sequential data, they are widely-used for air pollution prediction. However, designing appropriate LSTM architectures and hyperparameters for given tasks can be challenging, which are normally determined by users in existing LSTM-based methods. Note that Genetic Algorithm (GA) is an effective optimization technique, and local search in augmenting the global search ability of GA has been proved, which is rarely considered by existing GA-optimzied LSTM methods. In this work, simultaneous LSTM architecture and hyperparameter search based on GA and local search techniques is investigated for air pollution prediction. Specifically, a new LSTM model search method is designed, termed as HGA-LSTM. HGA is a hybrid GA, which is proposed by integrating GA with local search adaptively. Based on HGA, HGA-LSTM is developed to search for LSTM models with simultaneous LSTM architecture and hyperparameter optimization. In HGA-LSTM, a new crossover is designed to be adaptive to the variable-length representation of LSTM models. The proposed HGA-LSTM is compared with widely-used LSTM-based and nonLSTM-based prediction methods on UCI (University of California Irvine) datasets for air pollution prediction. Results show that HGA-LSTM is generally better than both types of reference methods with its evolved LSTM models achieving lower mean square/absolute errors. Moreover, compared with a baseline method (a GA without local search), HGA-LSTM converges to lower error values, which reflects that HGA has better search ability than GA.