Monitoring point optimization in lake waters

In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitor...

Full description

Saved in:
Bibliographic Details
Published in:Water science & technology. Water supply 2020-09, Vol.20 (6), p.2348-2358
Main Authors: Liu, Gaoxuan, Ai, Jiaoyan, Xu, Jun, Zheng, Jianwu, Yao, Dongyi
Format: Article
Language:English
Subjects:
Chlorophyll
Cluster analysis
Distribution
Environmental monitoring
Experiments
Genetic algorithms
Geography
Isometric
Lakes
Mean square errors
Monitoring systems
Multiple objective analysis
Neural networks
Optimization
Parameters
Radial basis function
Unmanned vehicles
Water monitoring
Water quality
Water quality management
Water temperature
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2020.147