Reinforcing long lead time drought forecasting with a novel hybrid deep learning model: a case study in Iran

Abstract

Drought assessment is inherently complex, particularly under the influences of climate change, which complicates long-term forecasting. This study introduces a novel hybrid deep learning model, Deep Feedforward Natural Networks (DFFNN), enhanced by War Strategy Optimization (WSO), aimed at forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) for lead times of one, three, six, nine, and twelve months. Key parameters of the DFFNN, including the number of neurons and layers, learning rate, training function, and weight initialization, were optimized using the WSO algorithm. The model’s performance was validated against two established optimizers: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Evaluations were conducted at two synoptic stations with distinct climatic conditions in Iran. Results demonstrated that the WSO-DFFNN model achieved superior performance for SPEI 12 (t + 1) with a correlation coefficient (r) of 0.9961 and Normalized Root Mean Square Error (NRMSE) of 0.1028; for SPEI 12 (t + 3) with r = 0.8856 and NRMSE = 0.1833; for SPEI 12 (t + 6) with r = 0.8573 and NRMSE = 0.2203; for SPEI 12 (t + 9) with r = 0.7951 and NRMSE = 0.2479; and for SPEI 12 (t + 12) with r = 0.7840 and NRMSE = 0.3279 at the Chabahar station. Additionally, the WSO-DFFNN model outperformed for SPEI 12 (t + 1) with r = 0.9118 and NRMSE = 0.1704; for SPEI 12 (t + 3) with r = 0.8386 and NRMSE = 0.2048; for SPEI 12 (t + 6) with r = 0.7602 and NRMSE = 0.2919; for SPEI 12 (t + 9) with r = 0.6379 and NRMSE = 0.2843; and for SPEI 12 (t + 12) with r = 0.6044 and NRMSE = 0.3463 at the Anzali station. The results obtained from this study have the potential to improve drought management strategies.