Mncube, ZXulu, SMbatha, Nkanyiso B2026-03-112026-03-112025-012673-6187https://doi.org/10.3389/frsen.2026.1723667http://hdl.handle.net/10204/14748Introduction: Increasing research demonstrates the value of nighttime light (NTL) data for studying human activities, including urban change. The public availability of these products on geospatial computing platforms like Google Earth Engine (GEE) has expanded their use for various applications and adding incorporation of Python and R analysis tools. Methods: Deep learning techniques such as Wavelet Denoise (WD), Empirical Mode Decomposition (EMD), and Enhanced Empirical Mode Decomposition (EEMD) are seldom used in NTL research, but here were used them with long short-term memory (LSTM) to form hybrid models to denoise and decompose NTL trajectory to interpretable frequency levels and intrinsic mode functions (IMFs) that improve trend evaluation. We leveraged these tools to assess the performance of deep learning models in modelling and forecasting NTL trends in Cape Town, Durban, and Johannesburg from 2014 to 2023. Root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate model performance. Results: The findings indicate that integrating decomposition approaches with LSTM enhances the precision and interpretability of NTL modelling. In Cape Town, the RMSE for all models varied from 0.083 to 0.114, while the MAE ranged from 0.063 to 0.085. Durban, RMSE ranged from 0.069 to 0.133, and MAE varied from 0.055 to 0.108. Johannesburg, RMSE ranged from 0.124 to 0.449 and MAE varied from 0.102 to 0.383. Discussion: Because of decomposition advantages, EEMD-LSTM hybrid model showing superior efficacy in Cape Town and Johannesburg, whilst EMD-LSTM model excelled in Durban.FulltextenHybrid deep learning modelsRoot mean square errorRMSEMean absolute errorMAENighttime lightNTLArticleN/A