金奈地区岩石深度空间变异性的确定

P. Samui, R. Viswanathan, J. Jagan, P. Kurup
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引用次数: 2

摘要

本研究采用普通克里格(OK)、广义回归神经网络(GRNN)、遗传规划(GP)和极小极大概率机回归(MPMR)四种建模技术对印度金奈的岩石深度(d)进行预测。纬度(Lx)和经度(Ly)被用作模型的输入。构造了一个半变异函数来发展OK模型。发展的GP给出了金奈任意点的d的预测方程。对四种建模技术进行了比较。MPMR的性能略好于其他模型。所建立的模型给出了金奈地区岩石深度的空间变异性。
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Determination of Spatial Variability of Rock Depth of Chennai
This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.
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