Water Quality Prediction Based on an Innovated Physical and Data Driving Hybrid Model at Basin Scale

IF 6 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Informatics Pub Date : 2024-01-31 DOI:10.3808/jei.202400510
Y. L. A, G. Q. Wang, Q. Z. Zhang, P. Z. Wang, B. L. Xue, Z. Y. Gao, Y. B. Peng
{"title":"Water Quality Prediction Based on an Innovated Physical and Data Driving Hybrid Model at Basin Scale","authors":"Y. L. A, G. Q. Wang, Q. Z. Zhang, P. Z. Wang, B. L. Xue, Z. Y. Gao, Y. B. Peng","doi":"10.3808/jei.202400510","DOIUrl":null,"url":null,"abstract":"The prediction of basin water quality has become an urgent need for water environment management, where water pollution is on the increase. Currently, physical models are primarily used for water quality predictions, but the models are not adaptable for the automatic future prediction of watershed water quality owing to their non-automatic boundary setting. The development of big data, which has led to artificial intelligence (AI) technology, has remedied the deficiency of physical models and has been widely used in water quality prediction. However, the accuracy of AI models depends only on the quantity and quality of dataset, which is applied on specific and discrete sections with enough data and difficult to extend to regions with limited monitoring data. Thus, we constructed migration and distribution gates to express the spatial influence of different variables from different sections on the water quality of a specific and discrete section. The temporal processes were expressed by degradation equation. The migration gate, distribution gate and degradation equations were incorporated into Long Short-Term Memory Network (LSTM) to improve the operation mechanism of the LSTM algorithm to create the Im-LSTM model, which considers both the temporal influence of a specific section and the spatial influence of other sections on a specific section at basin scale. Compared to ANN, LSTM, Im-LSTM showed the best performance for basin water quality prediction, especially for mainstream sections at sudden pollution process. Thus, the proposed Im-LSTM provides a new approach for water environment supervision.\n","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"14 1","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Environmental Informatics","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3808/jei.202400510","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The prediction of basin water quality has become an urgent need for water environment management, where water pollution is on the increase. Currently, physical models are primarily used for water quality predictions, but the models are not adaptable for the automatic future prediction of watershed water quality owing to their non-automatic boundary setting. The development of big data, which has led to artificial intelligence (AI) technology, has remedied the deficiency of physical models and has been widely used in water quality prediction. However, the accuracy of AI models depends only on the quantity and quality of dataset, which is applied on specific and discrete sections with enough data and difficult to extend to regions with limited monitoring data. Thus, we constructed migration and distribution gates to express the spatial influence of different variables from different sections on the water quality of a specific and discrete section. The temporal processes were expressed by degradation equation. The migration gate, distribution gate and degradation equations were incorporated into Long Short-Term Memory Network (LSTM) to improve the operation mechanism of the LSTM algorithm to create the Im-LSTM model, which considers both the temporal influence of a specific section and the spatial influence of other sections on a specific section at basin scale. Compared to ANN, LSTM, Im-LSTM showed the best performance for basin water quality prediction, especially for mainstream sections at sudden pollution process. Thus, the proposed Im-LSTM provides a new approach for water environment supervision.
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
基于创新物理和数据驱动混合模型的流域尺度水质预测
在水污染日益严重的情况下,流域水质预测已成为水环境管理的迫切需要。目前,水质预测主要采用物理模型,但由于模型的边界设置不自动化,无法适应未来流域水质的自动预测。大数据的发展带动了人工智能(AI)技术的发展,弥补了物理模型的不足,在水质预测中得到了广泛应用。然而,人工智能模型的准确性仅取决于数据集的数量和质量,适用于数据充足的特定离散断面,难以推广到监测数据有限的区域。因此,我们构建了迁移门和分布门来表达不同断面的不同变量对特定离散断面水质的空间影响。时间过程用退化方程表示。将迁移门、分布门和退化方程纳入长短期记忆网络(LSTM),改进 LSTM 算法的运行机制,创建 Im-LSTM 模型,该模型在流域尺度上既考虑了特定断面的时间影响,又考虑了其他断面对特定断面的空间影响。与 ANN、LSTM 相比,Im-LSTM 在流域水质预测方面表现最佳,尤其是在突发污染过程中的主流断面。因此,所提出的 Im-LSTM 为水环境监测提供了一种新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Environmental Informatics
Journal of Environmental Informatics ENVIRONMENTAL SCIENCES-
CiteScore
12.40
自引率
2.90%
发文量
7
审稿时长
24 months
期刊介绍: Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include: - Planning of energy, environmental and ecological management systems - Simulation, optimization and Environmental decision support - Environmental geomatics - GIS, RS and other spatial information technologies - Informatics for environmental chemistry and biochemistry - Environmental applications of functional materials - Environmental phenomena at atomic, molecular and macromolecular scales - Modeling of chemical, biological and environmental processes - Modeling of biotechnological systems for enhanced pollution mitigation - Computer graphics and visualization for environmental decision support - Artificial intelligence and expert systems for environmental applications - Environmental statistics and risk analysis - Climate modeling, downscaling, impact assessment, and adaptation planning - Other areas of environmental systems science and information technology.
期刊最新文献
Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning Impact of Carbon Emissions and Advance Payment on Optimal Decisions for Perishable Products via Parametric Approach of Interval Prediction of the Breeding and Wintering Ranges of Pomacea canaliculata in China Using Ensemble Models Decentralized Algae Removal Technologies for Lake Diefenbaker Irrigation Canals: A Review Real-Time LNG Buses Emissions Prediction Based on a Temporal Fusion Trans-Formers Model
×
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