基于神经网络的饮马河流域水质监测系统设计
Design of Water Quality Monitoring System in Yinma River Basin Based on Neural Network
杨拂晓 1王媛 2费龙 1刁思萌1
作者信息
- 1. 长春师范大学地理科学学院,吉林长春 130032
- 2. 吉林省环境科学研究院,吉林长春 130012
- 折叠
摘要
精细化的水质监测能够为水资源治理提供科学依据.人工智能、深度学习技术为水质智能监测开辟了新思路.本研究结合WebGIS与深度学习算法,设计并实现基于神经网络的水质监测系统.该系统以水质断面数据为数据支撑,通过划分数据集的方式训练神经网络模型,最终实现包括GIS空间操作模块、基础数据管理模块、水质识别模块、水质化学监测模块及水体三维可视化等功能的一体化监测系统.管理者与用户通过访问系统查询水质状况,实现远程实时监测.经过测试,系统达到预期效果,能够满足实际应用需求,有利于保护水域生态环境安全.
Abstract
Water Refined water quality monitoring can provide a scientific basis for water resources management.Artificial intelligence and deep learning technology have provided a new path for the intelligent monitoring of water quality.This study combines WebGIS and deep learning algorithms to design and implement a neural network-based water quality monitoring system.The system takes the water quality section data as the data support,trains the neural network model by dividing the data set,and fiinally realizes an integrated moni-toring system including GIS spatial operation module,basic data management module,water quality identification module,water quality chemical monitoring module and water body 3D visualization and other functions.Managers and users check the water quality status by visiting the system to realize remote real-time monitoring.After testing,the system can achieve the expected effect,which can meet the actual application needs,and is conducive to the protection of the ecological environment safety of the water area.
关键词
水质监测/神经网络/饮马河流域/GISKey words
water quality monitoring/neural network/Yinma River basin/GIS引用本文复制引用
基金项目
吉林省科技发展计划资助项目(20230203001SF)
出版年
2024
NETL
NSTL
万方数据