WD-PSTALSTM: a data-driven hybrid model for prediction of diesel vehicle NOx emissions

Abstract

Accurate prediction of transient nitrogen oxides (NOx) emissions from diesel vehicles is essential for precise emission inventories and effective pollution control but challenged by data nonlinearity and dynamic operating conditions. This study develops the Wavelet Decomposition (WD)-Parallel Spatiotemporal Attention-based Long Short-Term Memory (PSTALSTM) model, using real-world Portable Emission Measurement System (PEMS) and On-Board Diagnostics (OBD) data. WD preprocessing reduces emission data non-stationarity, generating more stable inputs. The PSTALSTM architecture, built upon Bidirectional Long Short-Term Memory (Bi-LSTM), incorporates a parallel attention mechanism to adaptively weight features and temporal segments, effectively capturing spatiotemporal correlations within the emission data. Validation with on-road test data demonstrates WD-PSTALSTM's superior performance over existing models. It achieves reductions exceeding 20 % in mean absolute error (MAE) and 15 % in root mean square error (RMSE), significantly enhancing prediction accuracy. These results establish WD-PSTALSTM as an effective approach for forecasting transient diesel engine NOx emissions. The research provides valuable methodologies for emission prediction based on vehicle operational data, contributing to environmental pollution mitigation efforts.