摘要
该文利用人工神经网络的BP模型建立了具有类似普通Kriging(OK)法和条件模拟(CS)运算目标的人工神经Kriging(NK)方法,在黄河河套平原进行了耕地和盐荒地初冻期、最大冻深期和融通期土壤水盐时空变异性的模拟和估值,通过NK法与OK法、CS法模拟、估值、检验结果及3种方法的理论变异函数、统计参数与实验变异函数的对比,结果表明NK法在消除滑动平均影响方面优于OK法,并以类似于CS法的空间变异性进行模拟,而且NK法有自身独特的优点,它不需要协方差函数的估计和变异函数的推求,对于含有一定特异值和一维到三维空间的扩展有更强的适应性,是对空间变异性应用研究方法的一种补充,同时拓宽了ANN的应用领域,具学科融合的优势。
Neural Kriging（NK） model was established by BP model of artificial neural network, which possesses the similarity of operational objectives to ordinary Kriging（OK） and Conditional Simulation（CS）. The NK model was a pplied to study the space variability of water-salt distribution during soil freezing and thawing periods-the freezing period, the maximum freezing depth period and thawing period in the cropland and non-cropland by simulating and testing sampling points and estimating unknown points. Comparing simulation, test and esti- mation results of NK model with that of OK model and conditional simulation and comparing semi-variogram of NK model with that of sampling value, OK estimated value and CS value. Results show that the NK method is better than OK method in eliminating moving-average effects. Furthermore, the NK method has itself particular advantages that do not require estimation of covariance function and semi-variogram treatment. At the same time, it has reasonably accurate estimation of prediction. So this method has more flexible adaptability for unique value and extend from one-dimensional to three-dimensional space than OK and CS method. And this method is a complement method for application of traditional space variability research. At the same time, it will broaden applied fields of artificial neural network （ANN） theory and has advantages of discipline interfusion.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2007年第7期48-53,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金(50179013)
内蒙古自治区自然科学基金项目(200408020519)
关键词
冻土水盐
空间变异性
人工神经网络
条件模拟
water and salt of frozen earth
space variability
artificial neural network
conditional simulation
