摘要
水利水电工程施工产生的如石油及其衍生物、挥发性有机化合物等污染物流入地下水中,会导致生态系统扰动或崩溃。受场地内地下水流动方向和速度的差异影响,增加了污染度检测的难度。为此,研究水利水电工程施工中地下水环境污染度检测方法。采用处理器、同步动态随机存储器模块和各类型传感器等组件,构建污染度检测模型,将标准化处理后的传感数据输入长短期记忆神经网络,获取污染度检测结果。经实验验证,检测模型能够准确取得不同检测目标的含量和数值,具有较强的可行性与实践性。
The pollutants produced by the construction of water conservancy and hydropower project,such as oil and its derivatives,volatile organic compounds,flow into groundwater,which will cause the disturbance or collapse of the ecosystem.Due to the difference in the direction and speed of water flow in the interior of the site,it is more difficult to detect the pollution degree.For this reason,the method of groundwater environmental pollution detection in the construction of water conservancy and hydropower project is studied.The pollution-degree detection model is constructed by using the processor,synchronous dynamic random access memory module and various types of sensors,and the standardized sensor data is input into the long and short term memory neural network to obtain the pollution-degree detection results.The experimental results show that the detection model can accurately obtain the content and value of different detection targets,which has strong feasibility and practice.
作者
曹宏斌
毛宏远
Cao Hongbin;Mao Hongyuan(Henan Haihe River Basin Water Resources Affairs Center,Xinxiang 453002,China)
出处
《环境科学与管理》
CAS
2024年第10期137-141,共5页
Environmental Science and Management
关键词
水利水电工程
地下水环境
污染度检测
长短期记忆网络
重金属元素
water conservancy and hydropower engineering
groundwater environment
pollution detection
long short-term memory network
heavy metal elements
