Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

Abstract

Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions.

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

“…Composite air quality prediction frameworks, integrating various machine learning and deep learning models, have shown even more promising outcomes. For example, combining long short-term memory networks with Gated Recurrent Unit model [27], random subspace [28], attention mechanism [29], XGBoosting tree [30], random forest regression based on linear regression [31], and an adaptive fuzzy inference system with the Extreme Learning Machine [32] has yielded substantial advancements. Phruksahiran, N [33] introduced the Geographically Weighted Prediction Method (GWP), leveraging optimal machine learning algorithms and additional prediction variables.…”

Deep-learning architecture for PM2.5 concentration prediction: A review

“…Sun et al. [ 68 ] proposed a deep residual learning framework for air quality prediction that used residual connections to improve the flow of information. Wu et al.…”

“…[ 54 ] 2022 Delhi, India BiLSTM H/S/T+1 15.59 - - - Sun et al. [ 68 ] 2019 Liaoning, China LSTM-DRSL H/S/T+1 10.53 9.09 20.05 - Lin et al. [ 46 ] 2020 Taiwan, China LSTM H/S/T+1 4.46 - 30.00 0.86 Park et al.…”

Using the Quarterly Compound Fractional Grey Model to Predict the Air Quality in 22 Cities of China

“…Zhou applied the Gaussian Process Mixture model to air quality prediction [13]. Sun and Xu proposed a deep random subspace learning framework based on longterm memory and combined the random subspace learning with the deep learning algorithm to build an air quality prediction model [14]. A multitask learning neural network model based on deep confidence network prediction training is used to predict pollutant concentrations in the atmosphere [15].…”