Ali Danandeh MehrSadra ShadkaniLaith AbualigahMir Jafar Sadegh SafariHazem MigdadyMehr, Ali DanandehMigdady, HazemShadkani, SadraSafari, Mir Jafar SadeghAbualigah, LaithDanandeh Mehr, Ali2025-10-0620242405-844010.1016/j.heliyon.2024.e341422-s2.0-85197631847http://dx.doi.org/10.1016/j.heliyon.2024.e34142https://gcris.yasar.edu.tr/handle/123456789/7916https://doi.org/10.1016/j.heliyon.2024.e34142Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues we propose a stabilized ANNs called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta T & uuml,rkiye. To enhance SANN forecasting accuracy we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities respectively.Englishinfo:eu-repo/semantics/openAccessDrought, Forecasting, ANN, Stabilizer, Signal decomposition, Variation mode decompositionDROUGHT, HYDROLOGYANNDroughtForecastingStabilizerSignal DecompositionVariation Mode DecompositionArticle