View in EDS HTML Full Text PDF Full Text

Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning.

Saved in:
Bibliographic Details
Title: Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning.
Authors: Bien, Tran Xuan1 (AUTHOR), Jaafari, Abolfazl2 (AUTHOR) jaafari@rifr-ac.ir, Van Phong, Tran3 (AUTHOR), Trinh, Phan Trong3 (AUTHOR), Pham, Binh Thai4 (AUTHOR) binhpt@utt.edu.vn
Source: Earth Science Informatics. Mar2023, Vol. 16 Issue 1, p131-146. 16p.
Subject Terms: *Water management, *Machine learning, *Groundwater, *Uplands, *Wells
Geographic Terms: Vietnam
Abstract: The sustainability of water resource management remains challenging in many regions around the world. Yet while the significance of groundwater potential maps in water resource management is well known, no agreed-upon approach has been suggested for the production of reliable, accurate maps of groundwater potential. In this study, we evaluated the Partial Decision Tree (PART), Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perception Network (MLP), Forest by Penalizing Attributes (FPA), and an ensemble version of the FPA method with the Decorate ensemble learning techniques (DFPA) for their capability to explore the associations between the locations of groundwater wells and a set of geo-environmental variables for the prediction of the potential for groundwater occurrence. We applied the methods to a spatially explicit dataset from five provinces of the Central Highlands, Vietnam. The results revealed that rainfall, land use/cover, elevation, and river density contributed most to groundwater potential in the study area. The ensemble model, i.e., DFPA, achieved greater goodness-of-fit and predictive ability than the single models. The ensemble DFPA model with accuracy = 70%, ROC-AUC = 0.77, RMSE = 0.44 provided the most accurate prediction of groundwater potential in the study area, followed by the FPA (ROC-AUC = 0.76), PART (ROC-AUC = 0.72), FURIA (ROC-AUC = 0.7), and MLP (ROC-AUC = 0.69) models, respectively. The ensemble DFPA model classified 34.7, 44.1, and 21.2% of the Central Highlands into low, moderate, and high potential categories, respectively. We experimentally showed that ensemble modeling is promising as a supporting tool in helping decision-makers, stakeholders, and researchers promote strategies for sustainable water resources management. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 162013327
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bien%2C+Tran+Xuan%22">Bien, Tran Xuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jaafari%2C+Abolfazl%22">Jaafari, Abolfazl</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jaafari@rifr-ac.ir</i><br /><searchLink fieldCode="AR" term="%22Van+Phong%2C+Tran%22">Van Phong, Tran</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Trinh%2C+Phan+Trong%22">Trinh, Phan Trong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pham%2C+Binh+Thai%22">Pham, Binh Thai</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> binhpt@utt.edu.vn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Mar2023, Vol. 16 Issue 1, p131-146. 16p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Groundwater%22">Groundwater</searchLink><br />*<searchLink fieldCode="DE" term="%22Uplands%22">Uplands</searchLink><br />*<searchLink fieldCode="DE" term="%22Wells%22">Wells</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Vietnam%22">Vietnam</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The sustainability of water resource management remains challenging in many regions around the world. Yet while the significance of groundwater potential maps in water resource management is well known, no agreed-upon approach has been suggested for the production of reliable, accurate maps of groundwater potential. In this study, we evaluated the Partial Decision Tree (PART), Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perception Network (MLP), Forest by Penalizing Attributes (FPA), and an ensemble version of the FPA method with the Decorate ensemble learning techniques (DFPA) for their capability to explore the associations between the locations of groundwater wells and a set of geo-environmental variables for the prediction of the potential for groundwater occurrence. We applied the methods to a spatially explicit dataset from five provinces of the Central Highlands, Vietnam. The results revealed that rainfall, land use/cover, elevation, and river density contributed most to groundwater potential in the study area. The ensemble model, i.e., DFPA, achieved greater goodness-of-fit and predictive ability than the single models. The ensemble DFPA model with accuracy = 70%, ROC-AUC = 0.77, RMSE = 0.44 provided the most accurate prediction of groundwater potential in the study area, followed by the FPA (ROC-AUC = 0.76), PART (ROC-AUC = 0.72), FURIA (ROC-AUC = 0.7), and MLP (ROC-AUC = 0.69) models, respectively. The ensemble DFPA model classified 34.7, 44.1, and 21.2% of the Central Highlands into low, moderate, and high potential categories, respectively. We experimentally showed that ensemble modeling is promising as a supporting tool in helping decision-makers, stakeholders, and researchers promote strategies for sustainable water resources management. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=162013327
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12145-022-00925-1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 131
    Subjects:
      – SubjectFull: Water management
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Groundwater
        Type: general
      – SubjectFull: Uplands
        Type: general
      – SubjectFull: Wells
        Type: general
      – SubjectFull: Vietnam
        Type: general
    Titles:
      – TitleFull: Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bien, Tran Xuan
      – PersonEntity:
          Name:
            NameFull: Jaafari, Abolfazl
      – PersonEntity:
          Name:
            NameFull: Van Phong, Tran
      – PersonEntity:
          Name:
            NameFull: Trinh, Phan Trong
      – PersonEntity:
          Name:
            NameFull: Pham, Binh Thai
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 18650473
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Earth Science Informatics
              Type: main
ResultId 1