摘要:
复杂岩溶地区的溶洞发育规模受地质构造、地区岩性、地下水动力系统等多种因素的影响,具有高度复杂性和非线性的特征.通过对岩溶区溶洞的赋存规律研究,确定影响溶洞发育规模的控制因素进行定量处理,收集已探明溶洞的样本数据.为克服已有研究对溶洞发育规模定性描述的模糊性,文章利用BP(BackPropagation)神经网络对自组织、自适应特性对数据样本的非线性关系揭示的能力,实现对溶洞发育规模的预测,并基于MATLAB实现BP神经网络结构的设计、训练、预测,其结果表明:BP神经网络模型对溶洞规模预测的精度高、收敛性能好.
Abstract:
In complex karst region,the size of karst cave is affected by many factors,such as geological structure,properties of soluble rock and groundwater hydrodynamic system and so on,which is characterized by high complexity and nonlinearity.Through the study of the occurrence and development of karst caves in karst area,the control factors affecting the scale of karst cave are determined and quantitatively analyzed,for which the data of proved caves are collected.In order to solve the problem with data fuzziness and descriptive formation of the karst caves,in this paper,the method of Back Propagation (BP) artificial neural network is employed to achieve the prediction of the scale of karst caves.As a BP neural network model is self-organization and self-adaptive,it is expected to handle the nonlinearity of sample data.The model is designed,tested,and applied,based on the MATLAB R2012a software.The results show that BP artificial neural network prediction model for the scale of karst cave is of high accuracy with its algorithm of good convergence.
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