View in EDS

A Novel Runoff Network Synchronous Prediction Model Based on Distribution Similarity Analyzing and Sparse Matrix Mapping.

Saved in:
Bibliographic Details
Title: A Novel Runoff Network Synchronous Prediction Model Based on Distribution Similarity Analyzing and Sparse Matrix Mapping.
Authors: Liu, Ling1,2 (AUTHOR) liul@zuwe.edu.cn, Wang, Jujie3 (AUTHOR) jujiewang@nuist.edu.cn, Li, Jianping4 (AUTHOR) ljp@ucas.ac.cn, Hao, Jun4,5 (AUTHOR) haojun@ucas.ac.cn, Yao, Yinhong6 (AUTHOR) yaoyinhong@cueb.edu.cn
Source: Water Resources Management. Jun2026, Vol. 40 Issue 8, p1-19. 19p.
Subject Terms: *Runoff analysis, *Sparse matrices, *Statistical correlation, *Forecasting, *Water management, *Spatiotemporal processes, *Flood warning systems
Abstract: Accurate runoff forecasting is critical for effective water resources planning and management. However, it remains challenging due to the complex spatiotemporal dynamics of hydrological networks. Here, we propose a novel runoff network synchronous prediction model that integrates a new sparse matrix mapping technique to capture spatial dependencies efficiently. This matrix comprises several sub-matrices derived from the distribution similarity analysis, effectively capturing disparities between sub-matrices as well as internal similarities. Ten benchmark models are used to evaluate the efficacy of the proposed methods. Results show that the proposed model has the best predictive performance, with mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe Efficiency Coefficient (NSE) values of 6.875, 12.381, 0.272, and 0.769, respectively. These findings offer practical implications for real-time flood warning systems. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
FullText Text:
  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 193809614
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Novel Runoff Network Synchronous Prediction Model Based on Distribution Similarity Analyzing and Sparse Matrix Mapping.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Ling%22">Liu, Ling</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liul@zuwe.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jujie%22">Wang, Jujie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jujiewang@nuist.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jianping%22">Li, Jianping</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> ljp@ucas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Hao%2C+Jun%22">Hao, Jun</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> haojun@ucas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Yao%2C+Yinhong%22">Yao, Yinhong</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> yaoyinhong@cueb.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Water+Resources+Management%22">Water Resources Management</searchLink>. Jun2026, Vol. 40 Issue 8, p1-19. 19p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Runoff+analysis%22">Runoff analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Sparse+matrices%22">Sparse matrices</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br />*<searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Flood+warning+systems%22">Flood warning systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate runoff forecasting is critical for effective water resources planning and management. However, it remains challenging due to the complex spatiotemporal dynamics of hydrological networks. Here, we propose a novel runoff network synchronous prediction model that integrates a new sparse matrix mapping technique to capture spatial dependencies efficiently. This matrix comprises several sub-matrices derived from the distribution similarity analysis, effectively capturing disparities between sub-matrices as well as internal similarities. Ten benchmark models are used to evaluate the efficacy of the proposed methods. Results show that the proposed model has the best predictive performance, with mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe Efficiency Coefficient (NSE) values of 6.875, 12.381, 0.272, and 0.769, respectively. These findings offer practical implications for real-time flood warning systems. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193809614
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11269-026-04737-6
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1
    Subjects:
      – SubjectFull: Runoff analysis
        Type: general
      – SubjectFull: Sparse matrices
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Water management
        Type: general
      – SubjectFull: Spatiotemporal processes
        Type: general
      – SubjectFull: Flood warning systems
        Type: general
    Titles:
      – TitleFull: A Novel Runoff Network Synchronous Prediction Model Based on Distribution Similarity Analyzing and Sparse Matrix Mapping.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Liu, Ling
      – PersonEntity:
          Name:
            NameFull: Wang, Jujie
      – PersonEntity:
          Name:
            NameFull: Li, Jianping
      – PersonEntity:
          Name:
            NameFull: Hao, Jun
      – PersonEntity:
          Name:
            NameFull: Yao, Yinhong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 09204741
          Numbering:
            – Type: volume
              Value: 40
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Water Resources Management
              Type: main
ResultId 1