Ali Danandeh MehrSadra ShadkaniLaith Mohammad Qasim AbualigahMir Jafar Sadegh SafariHazem Migdady2025-10-062024240584402405-844010.1016/j.heliyon.2024.e34142https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197631847&doi=10.1016%2Fj.heliyon.2024.e34142&partnerID=40&md5=68fde985288801d7c31d71635fa80c5ehttps://gcris.yasar.edu.tr/handle/123456789/8182Existing 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 Türkiye. 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. © 2024 Elsevier B.V. All rights reserved.EnglishAnn, Drought, Forecasting, Signal Decomposition, Stabilizer, Variation Mode DecompositionArticle