Yazar "Aksoy, Muhammed M." seçeneğine göre listele

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    Forecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study
    (Pergamon-Elsevier Science Ltd, 2025) Aksoy, Muhammed M.; Mowla, Najmul; Bilgili, Mehmet; Pinar, Engin; Durhasan, Tahir; Asadi, Davood
    Accurate modeling of near-surface air temperature (AT) trends is critical for assessing global and regional climate risks, particularly in light of the intensifying warming signals observed across the northern hemisphere and the tropics. This study proposes a robust and computationally efficient framework for forecasting near-surface AT across the global, the northern hemisphere, the southern hemisphere, and the tropics using two complementary time-series modeling techniques: seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks. The models are trained to capture both structured seasonal patterns and nonlinear temporal dynamics by leveraging the ERA5 reanalysis dataset (1970-2024) and incorporating preprocessing steps such as detrending and Z-score normalization. SARIMA consistently outperformed LSTM across most domains, particularly in the global region, achieving lower RMSE (0.0967 degrees C) and higher correlation (R = 0.9975), reflecting its superior capacity for linear and seasonal signal extraction. Quantitatively, SARIMA demonstrates 5%-10% lower RMSE and slightly higher correlation than LSTM across all domains, underscoring the statistical significance of its performance advantage. Projected near-surface AT anomalies by 2050 reveal a marked warming trend, with the SARIMA model estimating a global anomaly of +1.078 degrees C and a northern hemisphere anomaly of +1.474 degrees C, closely aligning with IPCC-reported trajectories and exceeding CMIP5 RCP4.5 projections. The findings underscore SARIMA's reliability for short-to mid-term near-surface AT forecasting and LSTM's potential for future hybrid modeling schemes. This work fills a critical methodological gap by integrating statistical rigor with scalable deep learning, offering enhanced fidelity for regional climate adaptation planning.
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    Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches
    (Pergamon-Elsevier Science Ltd, 2025) Bilgili, Mehmet; Pinar, Engin; Mowla, Md. Najmul; Durhasan, Tahir; Aksoy, Muhammed M.
    The accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970-2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.
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    Multimodal deep learning for estimating mean precipitable water vapor
    (Wiley, 2026) Mowla, Md. Najmul; Aksoy, Muhammed M.; Pinar, Engin; Bilgili, Mehmet; Durhasan, Tahir
    Precipitable water vapor (PWV) is a crucial atmospheric variable that influences weather systems, climate variability, and hydrological processes. Accurate PWV estimation is essential for improving numerical weather prediction, climate modeling, and remote-sensing applications. However, existing methods often rely on extensive meteorological inputs or computationally intensive architectures, limiting their applicability in data-sparse regions. This study introduces a novel hybrid framework, EMMA-NN-BiGRU-XGBoost, designed to forecast monthly mean PWV across Turkey using only four physically meaningful inputs: latitude, longitude, altitude, and seasonal indicators. The framework integrates an enhanced multimodal attention (EMMA) mechanism that disentangles spatial, altitudinal, and seasonal influences, improving interpretability and physical consistency. Bidirectional gated recurrent units (BiGRU) capture temporal dependencies, and XGBoost models nonlinear feature interactions within a weighted stacking ensemble. Hyperparameters are optimized via particle swarm optimization and Bayesian optimization, with particle swarm optimization demonstrating superior tuning efficiency. Extensive benchmarking against traditional machine-learning models, using grid search and random search with fivefold cross-validation, as well as deep-learning baselines, demonstrates significant improvements in predictive accuracy, achieving an root-mean-square error of and an of 0.92, representing a 15%-20% reduction in error compared with state-of-the-art methods. The model also exhibits robustness across diverse climatic zones in Turkey. Shapley additive explanations further elucidate feature importance, aligning model outputs with climatological principles. Beyond methodological advances, this work provides a scalable, interpretable, and data-efficient baseline for PWV forecasting, thereby facilitating enhanced climate diagnostics, hydrological risk assessments, and early warning systems, particularly in regions with limited meteorological observations.