华北水利水电大学学报(自然科学版)2024,Vol.45Issue(4) :102-108.DOI:10.19760/j.ncwu.zk.2024047

基于人工神经网络的沿海地区底泥盐度计算模型

Calculation Models for Sediment Salinity in Coastal Areas Based on Artificial Neural Networks

袁静 王锐 喻国良
华北水利水电大学学报(自然科学版)2024,Vol.45Issue(4) :102-108.DOI:10.19760/j.ncwu.zk.2024047

基于人工神经网络的沿海地区底泥盐度计算模型

Calculation Models for Sediment Salinity in Coastal Areas Based on Artificial Neural Networks

袁静 1王锐 1喻国良1
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作者信息

  • 1. 上海交通大学船舶海洋与建筑工程学院,上海 200240
  • 折叠

摘要

底泥盐度与海洋科学、河口研究、环境管理等密切相关,现有的底泥盐度计算公式存在精度不足、适用性有限等问题.为此,开展了 271组室内试验和10组户外试验,整合了其他学者的研究数据,以底泥电导率、泥沙浓度、温度和细颗粒表面系数为模型输入变量,分别建立了用于计算沿海地区底泥盐度的反向传播人工神经网络(BP-ANN)模型、粒子群优化的反向传播人工神经网络(PSO-BP-ANN)模型、结合遗传算法的反向传播人工神经网络(GA-BP-ANN)模型.与现有的底泥盐度计算公式相比,新建模型的精度更高,可为沿海地区底泥盐度的确定提供更多可供选择的预测方法.

Abstract

The salinity of sediment is closely related to marine science,estuarine research,and environmental manage-ment.The existing formulas for calculating sediment salinity have some problems,such as lack of accuracy and limited ap-plicability.In view of this,this study carried out 271 sets of laboratory tests and 10 sets of field tests,and integrated the research data of other scholars.With sediment conductivity,sediment concentration,temperature and surface coefficient of fine particles as input variables,the back propagation artificial neural network(BP-ANN)model,particle swarm optimiza-tion back propagation artificial neural network(PSO-BP-ANN)model and genetic algorithm combined back propagation artificial neural network(GA-BP-ANN)model for calculating sediment salinity in coastal areas were established respec-tively.Compared with the existing sediment salinity calculation formulas,the new models have higher calculation accuracy and provide more alternative prediction methods for determining sediment salinity in coastal areas.

关键词

底泥盐度/人工神经网络模型/反向传播/粒子群优化/遗传算法

Key words

sediment salinity/artificial neural network/back propagation/particle swarm optimization/genetic algorithm

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基金项目

国家水体污染控制与治理科技重大专项(2017ZX07206-003)

出版年

2024
华北水利水电大学学报(自然科学版)
华北水利水电大学

华北水利水电大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.558
ISSN:1002-5634
参考文献量10
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