Dalkilic, H. Yıldırım; Kilinc, H. Cagan; Yurtsever, Adem; Katipoğlu, O. Mert; Yeşilyurt, S. Nur

Daily streamflow forecasting is complicated by the fact that the predictive value of recent observations shifts considerably depending on the hydrological character of the catchment. This study develops a regime-aware, window-adaptive framework to compare conventional supervised learning algorithms — XGBoost, Random Forest, LSTM, and GRU — against four contrastive self-supervised (SSL) approaches: SimCLR, TS2Vec, CPC, and TempContrast. Experiments were conducted at four gauging stations in Türkiye, each representing a distinct hydroclimatic regime. The SSL stage employs a shared LSTM encoder pretrained with hydrology-informed augmentations and is evaluated across window lengths of 7, 14, 30, and 60 days, allowing a direct assessment of how context length interacts with catchment memory. A bias-regularised loss function is applied during fine-tuning to reduce systematic prediction errors — an aspect that has received limited attention in prior SSL-based hydrological studies. Model performance was quantified using R2, NSE, KGE, Willmott’s d, RMSE, MAE, MBE, and PBIAS. No single model emerged as universally superior. At the strongly seasonal Demirkapı station, supervised models reached Test R2 = 0.96–0.97, while SSL peaked near 0.91. At the low-flow, repetitive Beyşehir station, short-window SSL outperformed all supervised baselines, achieving Test R2 ≈ 0.93–0.94 against 0.89–0.90. At the noisier Akçay and Karadere stations, short-lag supervised models proved more reliable. These results point to a consistent pattern: the effectiveness of any given approach depends heavily on the memory structure and signal regularity of the basin in question, rather than on algorithmic complexity alone.

Asker, Mustafa; Ganjehsarabi, Hadi; Turhan Coban, M.

In this study, a numerical analysis of the inward solidification of phase change material inside spherical capsule is carried out. The spherical capsule that is subjected to convection from the outside surface initially is not at its melting temperature. The control volume approach and temperature transforming method are applied to solve the dimensionless energy equation. The model solution results are validated through a comparison with published experimental data for similar case and it shows a considerably good agreement. The analysis results show that the larger diameter spherical capsules have significantly greater solidification time compared to those with smaller diameters of spherical capsules. In addition, the entropy generation inside spherical capsule is analyzed. It is found that the entropy generation increases with increasing sphere capsule diameter, attains a maximum and then decreases.