基于神经网络的水流模型输入选择方法的研究

A. B. Dariane, Mohamadreza M. Behbahani
{"title":"基于神经网络的水流模型输入选择方法的研究","authors":"A. B. Dariane, Mohamadreza M. Behbahani","doi":"10.1080/23249676.2022.2088631","DOIUrl":null,"url":null,"abstract":"In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.","PeriodicalId":51911,"journal":{"name":"Journal of Applied Water Engineering and Research","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Development of an efficient input selection method for NN based streamflow model\",\"authors\":\"A. B. Dariane, Mohamadreza M. Behbahani\",\"doi\":\"10.1080/23249676.2022.2088631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.\",\"PeriodicalId\":51911,\"journal\":{\"name\":\"Journal of Applied Water Engineering and Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Water Engineering and Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23249676.2022.2088631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Water Engineering and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23249676.2022.2088631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 2

摘要

本文采用基于神经网络的径流模拟模型(NNSSM),对阿济柴河径流进行了模拟。选择合适的输入是开发NNSSM的重要一步。为此,与自组织映射(SOM)和二进制完全知情粒子群优化(BFIPSO)相比,我们研究了遗传分类算法(GCA)作为输入变量选择(IVS)方法的一种新应用。在另一个创新的应用程序中,我们使用SOM为选定数据的最终排名建立了社会选择(SC)。其次,通过添加季节性指数对模型进行了改进。结果表明了GCA的优越性。GCA的平均(最大)Nash-Sutcliffe指数为0.63(0.84),而SOM-SC和BFIPSO分别为0.55(0.71)和0.58(0.77)。此外,GCA每次运行所需时间不到30分钟,而SOM-SC和BFIPSO在相同情况下至少需要3和48小时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Development of an efficient input selection method for NN based streamflow model
In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.90
自引率
16.70%
发文量
31
期刊介绍: JAWER’s paradigm-changing (online only) articles provide directly applicable solutions to water engineering problems within the whole hydrosphere (rivers, lakes groundwater, estuaries, coastal and marine waters) covering areas such as: integrated water resources management and catchment hydraulics hydraulic machinery and structures hydraulics applied to water supply, treatment and drainage systems (including outfalls) water quality, security and governance in an engineering context environmental monitoring maritime hydraulics ecohydraulics flood risk modelling and management water related hazards desalination and re-use.
期刊最新文献
Hydrologic alteration by hydropower dams and climate change in the Sesan Basin Gravity current analyzer for lock-release experiments Modeling groundwater to assess the effect of artificial recharge (Kerbala, Iraq) Investigation on scouring and turbulence characteristics around T-head spur dike Streamflow forecast model using ANN
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1