摘要
为了解决多因素影响下岩溶塌陷预测评价问题,采用改进型BP-神经网络方法,推导带参数的神经网络公式,分析学习因子、动量因子和陡度因子的变化对系统收敛性的影响,确定最优的参数组合,并以唐山市区为例,建立了岩溶塌陷安全性评价模型.研究结果表明:基于改进型BP-神经网络的岩溶塌陷预测与实际情况吻合很好.该成果对中国地震多发区和地下水严重开采区的岩溶塌陷预测具有借鉴作用.
In order to solve the prediction and assessment problem of karst collapse under multi-factor conditions, this paper adopts the improved BP-neural network method, deduces the formula of ANN with parameters, and analyzes the impact of system convergence caused by variation of three factors of BP-neural network including learning factors, momentum factors and steepness factors, determines an optimal parameter combination, and constructs a safety assessment model of karst collapse in Tangshan City. The result of calculation shows very good consistency with actual conditions. The production holds a good reference to the karst collapse prediction of earthquake-prone areas and serious groundwater mining area in China.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2013年第1期1-6,共6页
Journal of Liaoning Technical University (Natural Science)
基金
中国地震局教师科研基金资助项目(20110113)
国家自然科学基金资助项目(41074072)
关键词
岩溶塌陷
预测评价
指标体系
改进型BP-神经网络
动量因子
学习因子
陡度因子
唐山市
karst collapse
prediction evaluation
index system
improved BP-neural network
momentum factor
learning factor
steepness factor
Tangshan city
