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. …”

Burn out assessment throughout demographic structure of the participant. by Nehal Mohamed Eisa (21148787)

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Demographic structure and Employment categorization. by Nehal Mohamed Eisa (21148787)

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Stack column chart represent Emotional Exhaustion through demographic structure. by Nehal Mohamed Eisa (21148787)

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Donut chart shows Demographic structure and Employment categorization. by Nehal Mohamed Eisa (21148787)

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i & ii: PCA represent the (i) 2d and (ii) 3d to identify the underlying dimensions of the lifestyle questions. by Nehal Mohamed Eisa (21148787)

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Stack column chart represent Depersonalization through demographic structure. by Nehal Mohamed Eisa (21148787)

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Stack column chart represent Personal Accomplishment through demographic structure. by Nehal Mohamed Eisa (21148787)

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Heatmap represents the criterion of health lifestyle survey. (Color guide is provided above each figure). by Nehal Mohamed Eisa (21148787)

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Comparison of SD method scores between three ECDs (<i>n</i> = 34; *<i>p</i> < 0.05 using Friedman test with Bonferroni’s correction). by Yong-Rae Kim (19797278)

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Fig 3 - by Yong-Rae Kim (19797278)

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S3 Table - by Yong-Rae Kim (19797278)

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The images of erosion control dams (ECDs) with three different exteriors. by Yong-Rae Kim (19797278)

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S5 Table - by Yong-Rae Kim (19797278)

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Fig 5 - by Yong-Rae Kim (19797278)

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S1 Table - by Yong-Rae Kim (19797278)

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