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    Assessing the efficacy of a sequential general variational mode decomposition-based combination model for United States wind power forecasting
    (Springer Nature Switzerland AG, 2026) Shalchilar, Mehdi; Samei, Rasoul; Ansari, Sanam; Ibrahim, Osama Ragab; Abdi, Erfan; Hayitov, Abdulla Nurmatovich; Xudaynazarov, Egambergan
    Accurate forecasting of wind energy is essential for maintaining grid stability and ensuring efficient energy management. Hybrid modeling approaches offer enhanced predictive accuracy and reliability, supporting wind farm performance, seam- less grid integration, and informed market operations. This study presents an adaptive forecasting system that compares hybrid and standalone models using a univariate framework to preserve simplicity and reduce computational overhead. Historical U.S. wind energy data spanning 2001–2025 were utilized, with optimal lag selection based on correlation coefficients exceeding 0.899. The dataset was partitioned into training and testing subsets in a 70:30 ratio. Two hybrid frameworks were implemented: one combining Convolutional Neural Networks with Long Short-Term Memory (CNN- LSTM), and another integrating Artificial Neural Networks with Sequential Variable Mode Decomposition (SVMD-ANN). Two standalone models were implemented: Support Vector Regression (SVR) and ANN. The Combination Model Based on Sequential General Variational Mode Decomposition is important for wind power prediction because it significantly enhances the accuracy and robustness of forecasting wind power output by effectively decomposing complex, non-sta- tionary wind power time series into intrinsic mode components. This decomposition allows more accurate modeling of different wind power signal characteristics and improved handling of fluctuations and abrupt changes commonly seen in wind power data. Performance was assessed using graphical visualizations and quantitative metrics, including Root Mean Square Error (RMSE), Coefficient of Determination (R²), symmetric Mean Absolute Percentage Error (sMAPE), and Mean Absolute Percentage Error (MAPE). The SVMD-ANN model achieved the highest accuracy, with a total error of 4.16 × 10⁵ MWh, R² of 0.964, sMAPE of 10.43%, and MAPE of 20.12%. CNN-LSTM showed competitive results (RMSE: 5.75 × 10⁵ MWh, R²: 0.952, sMAPE:16.26%, MAPE: 31.34%), outperforming ANN (RMSE: 5.79 × 10⁵ MWh, R²: 0.949, sMAPE:16.67%, MAPE: 34.76%) and SVR (RMSE: 5.91 × 10⁵ MWh, R²: 0.947, sMAPE:18.43%, MAPE: 45.44%). These findings validate the robustness of the proposed SVMD-ANN model and highlight its potential for modeling monthly wind energy production.
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    Comparative insights into independent and hybrid modeling strategies for effective river water level prediction and management
    (Springer Nature Switzerland AG, 2025-08-26) Ibrahim, Osama Ragab; Vafaei, Aynaz; Ansari, Sanam; Abdi, Erfan; Sifaei, Maryam; Jafari Mohammadi, Seyed Mahdi
    Accurate river water level prediction is essential for flood risk mitigation, efficient water allocation, and ecosystem protec- tion. It also enables assessment of climate change impacts on hydrological regimes, providing critical data for adaptation strategies. This study applies advanced forecasting models to two major rivers—the Mississippi and the Danube—charac- terized by distinct climatic and hydrological profiles, over 17 years (2008–2025). A rigorous data preprocessing protocol was implemented, involving outlier removal and imputation of missing values to ensure data integrity. For monthly water level prediction, two standalone models—deep neural network (DNN) and convolution neural network (CNN)—were used alongside two hybrid architectures: CNN with long short-term memory (CNN-LSTM) and CNN with bidirectional LSTM (CNN-BiLSTM). Input features included mean temperature, precipitation, relative humidity, and three lagged water level values. Model performance was evaluated using visual diagnostics and three statistical metrics: root mean square error (RMSE), Pearson’s correlation coefficient (r), and Nash–Sutcliffe efficiency (NSE). The CNN-BiLSTM model exhibited superior predictive capability. At the Danube station, it achieved an RMSE of 0.47 m, r of 0.94, and NSE of 0.88. At the Mississippi station, it yielded an RMSE of 0.93 m, r of 0.96, and NSE of 0.95. These results highlight the model’s robustness across diverse hydrological settings. The validation results show consistent performance and reliability. This modeling framework provides valuable support for water resources management, enabling informed planning and decision-making in these two basins.