中国岩溶2018，Vol.37Issue(1)：139-145,7.DOI:10.11932/karst2018y03
基于BP神经网络的溶洞规模预测及应用
Prediction model for the scale of karst cave based on back propagation artificial neural network and its application
摘要
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.关键词
溶洞/赋存规律/BP神经网络/规模预测Key words
karst cave/occurrence regularity/BP neural network/scale prediction分类
交通工程引用本文复制引用
刘振华,范宏运,朱宇泽,柳尚..基于BP神经网络的溶洞规模预测及应用[J].中国岩溶,2018,37(1):139-145,7.基金项目
国家自然科学基金面上基金(51479106) （51479106）
山东省自然科学基金(2014ZRE27303) （2014ZRE27303）
水文水资源与水利工程科学国家重点实验室开放研究基金(2016zd13) （2016zd13）
华南理工大学亚热带建筑科学国家重点实验室开放研究项目(2016ZB07) （2016ZB07）
