Description of the study sites in the NRE. by Jeong-Soo Gim (18058588)

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Table_1_Differential microbiome features in lake–river systems of Taihu basin in response to water flow disturbance.xlsx by Peng Xiao (172430)

“…Additionally, migration and dispersal rates of microbes in the ET region, along with the impact of dispersal limitations, were significantly higher than in the ST region. High flow disturbances notably reduced microbial niche width and overlap, decreasing the complexity and stability of microbial coexistence networks. …”

Image_1_Differential microbiome features in lake–river systems of Taihu basin in response to water flow disturbance.pdf by Peng Xiao (172430)

“…Additionally, migration and dispersal rates of microbes in the ET region, along with the impact of dispersal limitations, were significantly higher than in the ST region. High flow disturbances notably reduced microbial niche width and overlap, decreasing the complexity and stability of microbial coexistence networks. …”

Areal densities of single targets in the feeding areas of northern and southern resident killer whales. by Burak Saygili (19832062)

Image 1_Spawning habitat selection in Schizothorax wangchiachii using acoustic tagging and tracking.png by Bo Li (112195)

“…Random Forest-based importance analysis indicated that fluvial substrate composition and surface flow velocity were the key predictive variables for habitat selection model with MeanDecreaseGini being 23.3% and 22.6%, respectively.</p>Significance<p>These findings provide quantitative criteria for restoring natural spawning grounds and optimizing ecological operation strategies to support S. wangchiachii conservation in the lower Jinsha River.…”

Image 1_Effects of ultraviolet radiation as a climate variable on the geographic distribution of Oryza sativa under climate change based on Biomod2.tif by Rulin Wang (4814577)

“…Under the current climate conditions, the suitable habitats of O. sativa are mainly distributed in the south of the Yangtze River. In the future climate scenario, the total suitable habitat area of O. sativa tended to decrease, but the suitable distribution area under the influence of UV was larger than that without UV.…”

DNA Metabarcoding Unveils a Decade of Dietary Shifts in Prey Fishes of an Estuarine Dolphin by Jianqing Lin (19361335)

“…Furthermore, PERMANOVA confirmed significant temporal dietary divergence (P<0.05), with intensified niche partitioning between C. lucida and the other species, while dietary overlap between C. thrissa and K. punctatus decreased from 62% (2013) to 28% (2023). …”