Short-term drought Index forecasting for hot and semi-humid climate Regions: A novel empirical Fourier decomposition-based ensemble Deep-Random vector functional link strategy

The development of advanced technologies based on computer aid models in the domain of crops and agriculture productively is a modern advancement. Machine learning (ML) based forecasting of the short-term drought indicators (e.g., Standardized Precipitation Evapotranspiration Index (SPEI)) based on individual signals is a complex process that involves several factors including data quality and availability, inherent signal complexity, non-stationarity of climate, and uncertainty in ML models. Recent achievements in the field of Fourier-based signal processing integrated with advanced deep learning approaches have made it possible to produce very accurate intelligent frameworks for multi-temporal drought indicators and fill this gap. In this research, a new ultramodern complementary intelligent framework comprised of the SelectKbest feature selection (FS), Empirical Fourier Decomposition (EFD), and deep ensemble random vector functional link (Deep RVFL) was developed for multi-temporal monthly forecasting of short-term drought indicators for three and six months (SPEI3 and SPEI6) for two different very hot and semi-humid climate zones of Iran. For this purpose, the most influential time lags associated with each drought indicator were indicated using the SelectKbest FS in each zone. Afterwards, the individual SPEI signals were decomposed by the EFD technique imposing the most important lagged components to feed the ML approaches. Here, a new hybrid architecture deep learning model, namely a convolutional neural network coupled with a bidirectional gated recurrent unit (CNN-Bi-GRU) designed for multi-temporal drought forecasting in the next one-and three- months. Two advanced approaches, introducing the convolutional neural network coupled with bidirectional recurrent neural network (CNN-Bi-RNN), and Random vector function link (RVFL) were adopted to validate the main model (EFD-DeepRVFL) in complementary and standalone counterpart forms. The robustness of all the models was examined using several metrics such as coefficient of determination (R²), root mean square error (RMSE), reliability, and squared Chi-square Distance (SquD). The comprehensive assessment of the outcomes of hybrid schemes revealed that EFD-DeepRVFL owing to superior performance (R²|SPEI3(t + 1) = 0.953, R²|SPEI3(t + 3) = 0.837, R²|SPEI6(t + 1) = 0.962, and R²|SPEI6(t + 3) = 0.887) at Ahvaz station and (R²|SPEI3(t + 1) = 0.964, R²|SPEI3(t + 3) = 0.863, R²|SPEI6(t + 1) = 0.935, and R²|SPEI6(t + 3) = 0.839) at Kermanshah station outperformed the EFD-RVFL and CNN-Bi-RNN, respectively. The developed expert system provides early warning of drought conditions, as a decision-making tool, crop yield prediction, and water resources risk assessment.