Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China.

Predicting daily solar radiation using a novel hybrid Long Short-Term Memory network across four climate regions of China. [Display omitted] • MEA-DBN-LSTM was the most recommended R s estimated model across China. • DBN improved complex-based LSTM models but without impact on other input types. • MEA was significantly effective in optimizing the hyperparameters of DBN-LSTM. • Complex-based were superior to the sunshine-based and temperature-based R s models. • Temperature is the critical input for SMR, but sunshine is more important for other regions. Accurate daily solar radiation (R s) data is crucial for the growth and development of crops, but its direct observation is relatively scarce in most regions around the world. Therefore, accurately estimating daily R s is of great significance. Regarding the above practical requirement, this study developed a novel hybrid deep learning model (MEA-DBN-LSTM), which combined the Long Short-Term Memory model (LSTM), Deep Belief Network (DBN), and Mind Evolutionary Algorithm (MEA), to estimate the R s across four different climate regions of China using five easily accessible meteorological input combination (sunshine-based, temperature-based and three complex-based). The results showed that MEA-DBN-LSTM was proved to be the most recommended R s estimated model across four different climate regions of China, and the corresponding median R2, NSE, RMSE, MAE, and MAPE values ranged 0.805–0.999, 0.656–0.954, 1.069–4.289 MJ m−2 d–1, 0.889–3.532 MJ m−2 d–1, and 0.055–0.296 respectively. Correspondingly, compared to the empirical R s model, the improvement values of MEA-DBN-LSTM were 12.33–52.13 % for R2, 14.80–69.38 % for NSE, 33.75–69.77 % for RMSE, 36.03–62.70 % for MAE, and 42.40–66.67 % for MAPE, respectively. Moreover, the MEA-DBN-LSTM exhibits improvements in R2, NSE, RMSE, MAE, and MAPE, ranging 1.40–13.50 %, 1.27–24.79 %, 1.81–61.18 %, 1.90–61.30 %, and 2.31–57.55 %, respectively, compared to other forms of LSTM-type models. Specifically, this study found the DBN modules significantly improved the complex-based single LSTM model, but without impact on sunshine-based and temperature-based single LSTM models. Regarding the MEA modules, it was effective in the hyperparameter optimization compared to DBN-LSTM. Furthermore, significant accuracy discrepancies exist among R s models using different input combinations across four different climate regions, and the complex-based was generally superior to other input combinations. Overall, our findings can provide accurate daily R s across four different climate regions of China using easily accessible meteorological data, which is of great significance for crop water consumption estimation, agricultural water resources management, crop growth conditions optimization, and sustainable agricultural development. [ABSTRACT FROM AUTHOR]