Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
Races using kitefoil and windfoil surfboards have been in the Olympic Games for the first time in Paris 2024,signalling their relevance in sailing sports.However,the dynamics of these devices is yet not well understoo...Races using kitefoil and windfoil surfboards have been in the Olympic Games for the first time in Paris 2024,signalling their relevance in sailing sports.However,the dynamics of these devices is yet not well understood,in particular the influence on the hydrodynamic forces and moments of the distance of the foil to the free surface.Considering this,the present paper documents an experimental investigation in which forces and torque produced,under uniform flow,by a full-scale state-of-the-art hydrofoil(suitable both for kitesurf and windsurf)were measured.A range of velocities,angles of attack,and submergences were tested,leading to Froude numbers based on submergence with maximum values around five,a typical range in actual sailing conditions.From these tests,formulae for the hydrodynamic coefficients have been proposed.They can be used for developing Velocity Prediction Programs(VPP)for this kind of craft,a necessary tool to plan racing configurations and to analyze their racing performance.With the aim of making the experimental data useful for benchmarking numerical models and for future research on related topics such as foil ventilation and transition to turbulence,the specimen’s 3D file is provided as supplementary material to this paper.展开更多
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
文摘Races using kitefoil and windfoil surfboards have been in the Olympic Games for the first time in Paris 2024,signalling their relevance in sailing sports.However,the dynamics of these devices is yet not well understood,in particular the influence on the hydrodynamic forces and moments of the distance of the foil to the free surface.Considering this,the present paper documents an experimental investigation in which forces and torque produced,under uniform flow,by a full-scale state-of-the-art hydrofoil(suitable both for kitesurf and windsurf)were measured.A range of velocities,angles of attack,and submergences were tested,leading to Froude numbers based on submergence with maximum values around five,a typical range in actual sailing conditions.From these tests,formulae for the hydrodynamic coefficients have been proposed.They can be used for developing Velocity Prediction Programs(VPP)for this kind of craft,a necessary tool to plan racing configurations and to analyze their racing performance.With the aim of making the experimental data useful for benchmarking numerical models and for future research on related topics such as foil ventilation and transition to turbulence,the specimen’s 3D file is provided as supplementary material to this paper.
