Browsing by Author "Zhang, Yang"
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Item A color prediction model for mending materials of the Yuquan Iron Pagoda in China based on machine learning(Heritage Science, 2024-06-06) Liu, Xuegang; Liu, Yuhang; Wang, Ke; Zhang, Yang; Lei, Yang; An, Hai; Wang, Mingqiang; Chen, YuqiuDuring the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. The corrosion products of Yuquan Iron Pagoda are mainly Fe3O4, γ-FeO(OH), α-FeO(OH) and α-Fe2O3, with contents of 13.1, 16.1, 40.2 and 30.6%, respectively. Due to their structural stability and suitable color characteristics, Fe3O4 and α-Fe2O3 are selected as the primary raw materials for the repair material. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of α-Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics.Item Rapid Sea Level Rise in the Tropical Southwest Indian Ocean in the Recent Two Decades(Geophysical Research Letters, 2023-12-27) Huang, Lei; Zhuang, Wei; Lu, Wenfang; Zhang, Yang; Edwing, Deanna; Yan, Xiao-HaiIt has been reported that the sea level falls in the tropical Southwest Indian Ocean (SWIO) from the 1960s to the early 2000s. However, a rising trend of 4.05 ± 0.56 cm/decade has occurred during the recent two decades with our analysis showing that manometric sea level contributes 41% to this sea level rise. 30% of this rise is due to steric sea level (SSL) change in the upper 2,000 m with SSL rise in the upper 300 m of secondary importance. Conversely, thermal expansion below the thermocline (300–2,000 m), likely caused by water mass spread from the Southern Ocean, induces major contribution to SSL changes. Compared to existing studies demonstrating the contribution of thermal variations above the thermocline to sea level variability in the tropical SWIO, this study emphasizes the importance of ocean mass and deeper ocean changes in a warming climate. Key Points - Rapid sea level rise occurs in the tropical Southwest Indian Ocean (SWIO) since the early 2000s - The ocean mass addition and the upper 2,000 m ocean warming contribute significantly to the total sea level rise - The upper 2,000 m ocean warming is primarily attributed to thermal expansion below the thermocline associated with the spread of water masses Plain Language Summary Global ocean sea level change is spatially and temporally nonuniform due to oceanic and atmospheric dynamics. The tropical Southwest Indian Ocean (SWIO) experienced a sea level fall from the 1960s to the early 2000s. However, a rapid sea level rise has occurred over the last two decades in the tropical SWIO that is faster than the global average. The ocean mass increase due to extra water input leads to an essential impact on sea level rise in the tropical SWIO. Compared to previous studies demonstrating the effect of thermal expansion in the upper 300 m, this study shows larger contributions from deeper ocean (300–2,000 m) warming over the past two decades. Overall, this study highlights the importance of ocean mass and deeper water thermal structure in regulating tropical SWIO sea level rise in a changing climate, as well as the need for observations and direct assessment of the abyssal ocean beneath 2,000 m.