CAJ | 학술논문

利用点估计模型、线性回归模型、非线性回归模型和人工神经网络模型等四种PTF_S分别预测大沽河流域90个土壤样本的田间持水量(θ_(-30 kPa))和凋萎含水量(θ_(-1 500 kPa)),借助传统统计学和地统计学方法对其空间变异性进行了比较分析.传统统计学分析认为非线性回归模型预测的效果最好,无论是实测值还是估计值,所有土壤样本θ_(-30 kPa)的变异系数总是小于θ_(-1 500 kPa),两者均属于中等变异性;地统计学分析表明实测值和预测值的θ_(-30 kPa)和θ_(-1 500 kPa)均存在不同程度的块金效应,且θ_(-30 kPa)总是表现出较θ_(-1 500 kPa)更强烈的空间相关性,通过分析θ_(-30 kPa)和θ_(-1 500 kPa)的半方差函数模型参数,发现人工神经网络模型最能真实地反映试验区土壤持水特性的空间变异性特征.
이용점고계모형、선성회귀모형、비선성회귀모형화인공신경망락모형등사충PTF_S분별예측대고하류역90개토양양본적전간지수량(θ_(-30 kPa))화조위함수량(θ_(-1 500 kPa)),차조전통통계학화지통계학방법대기공간변이성진행료비교분석.전통통계학분석인위비선성회귀모형예측적효과최호,무론시실측치환시고계치,소유토양양본θ_(-30 kPa)적변이계수총시소우θ_(-1 500 kPa),량자균속우중등변이성;지통계학분석표명실측치화예측치적θ_(-30 kPa)화θ_(-1 500 kPa)균존재불동정도적괴금효응,차θ_(-30 kPa)총시표현출교θ_(-1 500 kPa)경강렬적공간상관성,통과분석θ_(-30 kPa)화θ_(-1 500 kPa)적반방차함수모형삼수,발현인공신경망락모형최능진실지반영시험구토양지수특성적공간변이성특정.
Field water retention capacities (θ_(-30 kPa)) and wilting coefficients (θ_(-1 500 kPa)) of ninety soil samples in the Dagu River Basin were predicted separately with four PTF_S, i.e. point regression method, linear regression method, nonlinear regression method and artificial neural network method, and their spatial variabilities were analyzed with the aid of traditional statistic and geostatistic methods. The traditional statistics revealed that the nonlinear regression method was the best with the variation coefficients of θ_(-30 kPa) of all the soil samples, being always less than θ_(-1 500 kPa), however, no matter measured or predicted values, both belonged to the category of moderate in spatial variability. The geostatistics also showed that both measured and predicted θ_(-30 kPa) and θ_(-1 500 kPa) demonstrated varied nugget effects, moreover, θ_(-30 kPa) always had stronger spatial dependence than θ_(-1 500 kPa) did. Analysis of the parameters of semi-variance model for θ_(-30 kPa) and θ_(-1 500 kPa) ultimately revealed that the artificial neural network model could most truthfully characterize spatial variability of the soil water retention capability in the experimental zone.