Predicting normalized difference vegetation index using a deep attention network with bidirectional GRU: a hybrid parametric optimization approach

Abstract

Abstract Scalable and accurate normalized difference vegetation index (NDVI) prediction is necessary to track the status of vegetation and the environment and to support proper ecological management. Herein, we present an innovative deep-learning approach to improve NDVI prediction performances by considering enhanced temporal modeling and hybrid optimization processes. The analysis is based on a core model that integrates a Bidirectional Gated Recurrent Unit (BiGRU) with the profound attention feature since the primary research incorporates the capability of complex temporal in addition to NDVI-time series value. The model performs better through a dual algorithm combining the waterwheel plant algorithm (WWPA) and statistical fractal search (SFS) named WWPASFS-BiGRU. The proposed approach is evaluated using real-world NDVI datasets, demonstrating its capability to outperform traditional models and state-of-the-art deep learning methods. Key performance metrics highlight the model’s accuracy, with a root mean square error (RMSE) as low as 0.00011, reflecting its superior predictive ability. Comparative experiments showcase the robustness of our model across different environmental conditions and geographical settings, affirming its applicability in diverse ecological forecasting scenarios. Additionally, extensive statistical validation, including ANOVA and Wilcoxon tests, confirms the model’s consistency and reliability. The effectiveness of the WWPASFS-BiGRU model is illustrated through applications in predicting NDVI trends across regions in Saudi Arabia, providing critical insights for ecosystem management and sustainable development planning.