摘要
通过对乌江镇34号泉的水文地质勘查及地下水污染监测表明,因位于该泉含水系统补给区的4#磷石膏堆场产生岩溶渗漏,使34号泉出现了磷污染。在确定了渗漏污染为岩溶裂隙管道线状弥散运移类型的基础上,建立了34号泉岩溶渗漏污染的BP神经网络预测模型,使用不同的训练函数对34号泉磷污染进行预测评价,预测结果接近实际,为乌江渡至楠木渡河段水污染防治提供了依据。
The hydrogeological exploration and groundwater pollution monitoring in No.34 spring of Wujiang town shows that karst leakage has occurred in the 4# phosphogypsumslag site located in the recharge area of the aquifer system,which caused the phosphorus pollution in No. 34 spring. So we established the BP neural network forecasting model of No.34 spring karst leakage pollution based on the dispersive-migration of karst fissure-conduit. It useed different training functions to forecast and evaluate the No. 34 springs phosphorus pollution. Because the prediction is close to reality,the result can provide the basis for water pollution prevention between Wujiang river and Nanmu river.
作者
叶婷婷
丁坚平
褚学伟
YE Tingting DING Jianping ZHU Xuewei(College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, Chin)
出处
《贵州大学学报(自然科学版)》
2017年第3期115-120,共6页
Journal of Guizhou University:Natural Sciences
基金
贵州省科技支撑计划(黔科合支撑[2017]2858)
贵州省水利厅科技专项经费项目资助(KT201506)
贵州大学研究生创新基金(研理工2017046)
关键词
磷污染
BP神经网络
预测
phosphorus pollution
BP neural network
prediction