View in EDS

Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models

Saved in:
Bibliographic Details
Title: Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
Authors: Khosravi, Khabat, Farooque, Aitazaz A., Naghibi, Amir, Heddam, Salim, Sharafati, Ahmad, Hatamiafkoueieh, Javad, Abolfathi, Soroush
Contributors: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Water Resources Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Teknisk vattenresurslära, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Water, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Vatten, Originator, Lund University, Faculty of Social Sciences, Departments of Administrative, Economic and Social Sciences, Centre for Advanced Middle Eastern Studies (CMES), Lunds universitet, Samhällsvetenskapliga fakulteten, Samhällsvetenskapliga institutioner och centrumbildningar, Centrum för Mellanösternstudier (CMES), Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MECW: The Middle East in the Contemporary World, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MECW: The Middle East in the Contemporary World, Originator
Source: Ecological Informatics. 85
Subject Terms: Engineering and Technology, Civil Engineering, Geotechnical Engineering and Engineering Geology, Teknik, Samhällsbyggnadsteknik, Geoteknik och teknisk geologi
Description: Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). The results demonstrate that the BA-Kstar ensemble model achieved superior performance (R2 = 0.91, RMSE = 1.60, NSE = 0.91, and RSR = 0.30). Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. This study underscores the reliability and efficacy of hybrid ML models for Ep forecasting, with significant implications for their broader application in diverse climates and geographical regions.
Access URL: https://doi.org/10.1016/j.ecoinf.2024.102933
Database: SwePub
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://doi.org/10.1016/j.ecoinf.2024.102933#
    Name: EDS - SwePub (s4221598)
    Category: fullText
    Text: View record in SwePub
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edsswe&genre=article&issn=15749541&ISBN=&volume=85&issue=&date=20250101&spage=&pages=&title=Ecological Informatics&atitle=Enhancing%20Pan%20evaporation%20predictions%3A%20Accuracy%20and%20uncertainty%20in%20hybrid%20machine%20learning%20models&aulast=Khosravi%2C%20Khabat&id=DOI:10.1016/j.ecoinf.2024.102933
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Khosravi%20K
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsswe
DbLabel: SwePub
An: edsswe.oai.portal.research.lu.se.publications.cb0443a8.e379.4ee0.a64d.f65bf9d563f5
RelevancyScore: 1065
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1064.736328125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Khosravi%2C+Khabat%22">Khosravi, Khabat</searchLink><br /><searchLink fieldCode="AR" term="%22Farooque%2C+Aitazaz+A%2E%22">Farooque, Aitazaz A.</searchLink><br /><searchLink fieldCode="AR" term="%22Naghibi%2C+Amir%22">Naghibi, Amir</searchLink><br /><searchLink fieldCode="AR" term="%22Heddam%2C+Salim%22">Heddam, Salim</searchLink><br /><searchLink fieldCode="AR" term="%22Sharafati%2C+Ahmad%22">Sharafati, Ahmad</searchLink><br /><searchLink fieldCode="AR" term="%22Hatamiafkoueieh%2C+Javad%22">Hatamiafkoueieh, Javad</searchLink><br /><searchLink fieldCode="AR" term="%22Abolfathi%2C+Soroush%22">Abolfathi, Soroush</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Water Resources Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Teknisk vattenresurslära, Originator<br />Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Water, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Vatten, Originator<br />Lund University, Faculty of Social Sciences, Departments of Administrative, Economic and Social Sciences, Centre for Advanced Middle Eastern Studies (CMES), Lunds universitet, Samhällsvetenskapliga fakulteten, Samhällsvetenskapliga institutioner och centrumbildningar, Centrum för Mellanösternstudier (CMES), Originator<br />Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MECW: The Middle East in the Contemporary World, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MECW: The Middle East in the Contemporary World, Originator
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>Ecological Informatics</i>. 85
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Engineering+and+Technology%22">Engineering and Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Civil+Engineering%22">Civil Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Geotechnical+Engineering+and+Engineering+Geology%22">Geotechnical Engineering and Engineering Geology</searchLink><br /><searchLink fieldCode="DE" term="%22Teknik%22">Teknik</searchLink><br /><searchLink fieldCode="DE" term="%22Samhällsbyggnadsteknik%22">Samhällsbyggnadsteknik</searchLink><br /><searchLink fieldCode="DE" term="%22Geoteknik+och+teknisk+geologi%22">Geoteknik och teknisk geologi</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). The results demonstrate that the BA-Kstar ensemble model achieved superior performance (R2 = 0.91, RMSE = 1.60, NSE = 0.91, and RSR = 0.30). Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. This study underscores the reliability and efficacy of hybrid ML models for Ep forecasting, with significant implications for their broader application in diverse climates and geographical regions.
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doi.org/10.1016/j.ecoinf.2024.102933" linkWindow="_blank">https://doi.org/10.1016/j.ecoinf.2024.102933</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.portal.research.lu.se.publications.cb0443a8.e379.4ee0.a64d.f65bf9d563f5
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ecoinf.2024.102933
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Engineering and Technology
        Type: general
      – SubjectFull: Civil Engineering
        Type: general
      – SubjectFull: Geotechnical Engineering and Engineering Geology
        Type: general
      – SubjectFull: Teknik
        Type: general
      – SubjectFull: Samhällsbyggnadsteknik
        Type: general
      – SubjectFull: Geoteknik och teknisk geologi
        Type: general
    Titles:
      – TitleFull: Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Khosravi, Khabat
      – PersonEntity:
          Name:
            NameFull: Farooque, Aitazaz A.
      – PersonEntity:
          Name:
            NameFull: Naghibi, Amir
      – PersonEntity:
          Name:
            NameFull: Heddam, Salim
      – PersonEntity:
          Name:
            NameFull: Sharafati, Ahmad
      – PersonEntity:
          Name:
            NameFull: Hatamiafkoueieh, Javad
      – PersonEntity:
          Name:
            NameFull: Abolfathi, Soroush
      – PersonEntity:
          Name:
            NameFull: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Water Resources Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Teknisk vattenresurslära, Originator
      – PersonEntity:
          Name:
            NameFull: Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Water, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Vatten, Originator
      – PersonEntity:
          Name:
            NameFull: Lund University, Faculty of Social Sciences, Departments of Administrative, Economic and Social Sciences, Centre for Advanced Middle Eastern Studies (CMES), Lunds universitet, Samhällsvetenskapliga fakulteten, Samhällsvetenskapliga institutioner och centrumbildningar, Centrum för Mellanösternstudier (CMES), Originator
      – PersonEntity:
          Name:
            NameFull: Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MECW: The Middle East in the Contemporary World, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MECW: The Middle East in the Contemporary World, Originator
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 15749541
            – Type: issn-locals
              Value: SWEPUB_FREE
            – Type: issn-locals
              Value: LU_SWEPUB
          Numbering:
            – Type: volume
              Value: 85
          Titles:
            – TitleFull: Ecological Informatics
              Type: main
ResultId 1