- 期刊
以群集分析加強van Genuchten模式參數推估之研究
A Study of Using Cluster Analysis to Improve Parameters Prediction of van Genuchten Model
摘要
本研究旨在提升以連續土壤轉換函數（Continuous PTFs）預測van Genuchten模式（vG-Model）參數之能力。本研究針對中質地土壤進行分析，為解決三角質地圖界線上質地界定之模糊地帶，運用K-Means群集分析法依據粒徑分布範圍做分類，取代依質地做分組。利用複線性迴歸分析發展分組前後之模式比較，結果顯示於參數α之預測，分組模式確實能提升整體預測力，而參數n之預測，分組模式則未能精進預測能力；另發現黏粒含量（C）對於n值具有一定影響性，整體預測力與n值本身具有不確定性有關。本研究進一步與國內以區域性發展之Continuous PTFs比較，結果仍以粒徑分布範圍分組之模式預測力較佳，更印證模式發展前土樣分類之必要性。
並列摘要
The purpose of this study is improving the ability of Continuous PTFs used to predict the parameters of van Genuchten model. This study focused on medium-texture soils. For solving the fuzzy area on a triangular texture figure, K-Means cluster analysis was used to classify samples according to particle size distribution rather than texture. Multiple leaner regression was used to develop models which included all samples and classified samples. The result showed that the classified models could improve the prediction in parameter α, however, they could do so in parameter n. Observation of each model revealed that the parameter n was affected by clay content. This study further compared continuous PTFs which were developed according to region, and the result showed that the models which were developed based on particle size distribution had better prediction. We also proved that classifying samples is necessary before developing a model.