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標題: 以連續土壤轉換函數預測 van Genuchten 模式參數之研究
A Study of Using Continuous Pedotransfer Functions to Predict Parameters of van Genuchten Model.
作者: 林可薇
Lin, Ke-Wei
關鍵字: 土壤轉換函數
Pedotransfer functions
van Genuchten 模式(vG-Model)
van Genuchten Model(vG-Model)
Cluster analysis
Multiple linear regression
出版社: 水土保持學系所
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摘要: 本研究旨在以土壤質地為基礎,發展van Genuchten 模式(vG-Model)參數之連續土壤轉換函數(Continuous PTFs),台灣土壤型態以壤土為主,故針對中質地土壤進行分析。藉由廣泛蒐集國內外文獻確立模式內使用之變數,運用K-Means法群集分析將土樣依砂粒與黏粒含量做分類,共分成兩組,再利用複線性迴歸分析發展模式,並與未分類土樣發展之模式比較其預測力。由驗證組之均方根誤差(RMSE)與無偏估線點位分布結果顯示依質地分類之Continuous PTFs確實能增進參數α之預測,進而探討模式變數發現重力水與α具有線性關係;參數n之模式則無明顯之效果,觀察其模式發現n值可能受黏粒含量(C)影響。將驗證組土樣依黏粒含量重新分組後再帶入模式,其RMSE與無偏估線點位對於C值大於8%之土樣確實有精進之效果,C值小於8%則無此現象,此與n值之模式發展具有不確定性有關,但黏粒含量對於此參數仍有相當之影響。本研究進一步比較質地發展之PTFs與前人研究依區域發展之PTFs比較,結果顯示質地分類發展之模式於預測參數α與n皆明顯較區域性發展之模式佳,更證實質地分類之必要性。本研究發展之模式如下所示: ln(α)=-3.2110-0.1871∙(BD∙FC)-1423.24∙〖FC〗^(-2) +0.14523∙(BD∙f)-0.1525∙〖OM〗^(-2) ln(n)=-0.0605+407.7189∙f^(-2)+0.0227∙〖OM〗^(-2) +0.00013∙(Sa∙C) ln(α)=-4.3389-0.1572∙(BD∙FC)+0.1259∙(BD∙f) -0.2197∙〖OM〗^(-2) ln⁡(n)=-0.1049+0.0272∙〖OM〗^(-2)+437.1857∙f^(-2) +0.00052∙(C∙FC) ln(α)=-5.6336- 0.0058∙〖FC〗^2+0.0052∙(f∙FC) ln⁡(n-1)=8.6704+5.1255∙〖BD〗^2+850.0738∙〖FC〗^(-2) -16.5089∙BD+0.01316∙(BD∙FC) +0.4315∙(C∙BD)-0.5646∙C
The purpose of this study is developing pedotransfer functions (PTFs) which were based on soil texture to derive parameters of van Genuchten Model(vG-Model). The study focused on loamy soil, because it is the main type of soil in Taiwan. Determining variables in PTFs by collecting literatures widely, then K-Means cluster analysis was used to classify samples according to the content of sand and clay. All samples were derived into two groups, multiple linear regression analysis was used to develop model for each group, and then compared the predictive ability with the model which was developed from all samples. By the calculation of root mean square error and the point distribution of unbiased estimate line which produced from validations showed that PTFs developed according to texture could surely improve the prediction of the parameter α. Further investigation showed that there might be linear relationship between soil gravity water and parameter α. There was no improvement in the prediction of parameter n. However, the result of observing the model showed that the parameter n might be affected by the content of clay. Taking validations to regroup according to the content of clay, and then put them in the model again. The result of root mean square error and the point distribution of unbiased estimate line showed that when the content of clay was greater than 8%, the improvement of the model was obvious, but when the contents of clay were less than 8%, it didn’t have any effect. This phenomenon was related with that the previous studies which pointed out there was uncertainty in developing the model of n, but the effect of clay was undeniable. This study further compared two PTFs which were developed according to texture and region, and the result showed that in prediction of parameter α and n, the former was better than the latter. And the result proved that it was necessary to classify samples by texture. PTFs were as follows: ln(α)=-3.2110-0.1871∙(BD∙FC)-1423.24∙〖FC〗^(-2) +0.14523∙(BD∙f)-0.1525∙〖OM〗^(-2) ln(n)=-0.0605+407.7189∙f^(-2)+0.0227∙〖OM〗^(-2) +0.00013∙(Sa∙C) ln(α)=-4.3389-0.1572∙(BD∙FC)+0.1259∙(BD∙f) -0.2197∙〖OM〗^(-2) ln⁡(n)=-0.1049+0.0272∙〖OM〗^(-2)+437.1857∙f^(-2) +0.00052∙(C∙FC) ln(α)=-5.6336- 0.0058∙〖FC〗^2+0.0052∙(f∙FC) ln⁡(n-1)=8.6704+5.1255∙〖BD〗^2+850.0738∙〖FC〗^(-2) -16.5089∙BD+0.01316∙(BD∙FC) +0.4315∙(C∙BD)-0.5646∙C
其他識別: U0005-0708201217302600
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