Structure diagram of ensemble model. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Fitting formula parameter table. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Test plan. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Fitting surface parameters. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Model generalisation validation error analysis. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Empirical model prediction error analysis. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Fitting curve parameters. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Test instrument. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Empirical model establishment process. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Model prediction error trend chart. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Basic physical parameters of red clay. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

BP neural network structure diagram. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Structure diagram of GBDT model. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Model prediction error analysis index. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Fitting curve parameter table. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Model prediction error analysis. حسب Hongqi Wang (2208238)

"…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"

Design of the D-trial. حسب Torsten Schober (20485754)

"…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …"

Estimated mean values for light interception. حسب Torsten Schober (20485754)

"…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …"

Raw data V-trial. حسب Torsten Schober (20485754)

"…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …"

Raw data D-trial. حسب Torsten Schober (20485754)

"…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …"