Coupling Machine Learning and Detrended Cumulative Drought Index for Lake-Area Prediction
Rattachement africain : cn, bd, jp, my. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Lakes play a crucial role in supplying water resources, regulating regional climates, and supporting ecosystems. However, they are increasingly threatened by recurrent droughts. This study focuses on the Mu Us Sandy Land, the fourth largest desert in China, and presents an approach that combines machine-learning techniques with a newly constructed drought index—the detrended cumulative standardized precipitation-evapotranspiration index (DeCumSPEI)—to forecast monthly lake-area variations from 2001 to 2020. Remote sensing data, including lake-area measurements, were obtained using the Google Earth Engine platform. In addition, meteorological factors—such as precipitation, temperature, and actual evapotranspiration (ETa) as well as anthropogenic variables, such as crop evapotranspiration, the normalized difference vegetation index, and land-use and land-cover change—were collected. The study assessed the performance of six machine-learning models using fivefold cross validation: gradient boosting decision tree, extra trees, random forest, adaptive boosting (AB), bootstrap aggregating (Bagging), and eXtreme gradient boosting. These models were evaluated for their ability to predict lake areas under both short-term (monthly) and long-term (annual) drought conditions. In addition, the influence of ETaas an upper boundary condition was investigated. The results show that: First, all models, except for AB and Bagging, demonstrated strong predictive performance, achieving coefficients of determination (R2) as high as 0.833, and the lowest average root-mean-square error and standard deviation of 1.05 and 1.01 km2. Second, incorporating the 12-month scale DeCumSPEI significantly enhanced model accuracy, with performance improvements of up to 32.01%. Third, comparing models with and without ETaconfirmed the critical role of ETain improving prediction accuracy. These findings offer valuable insights for future lake area forecasting in drought-affected regions and underscore the potential of machine-learning models in hydrological and drought response research.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Coupling Machine Learning and Detrended Cumulative Drought Index for Lake-Area Prediction
- Date Crossref
- 01/01/2026
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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China Agricultural University State Key Laboratory of Efficient Utilization of Agricultural Water Resources pays non établi dans la noticeUniversité ou école supérieure
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Zhejiang Normal University pays non établi dans la noticeUniversité ou école supérieure
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Shahjalal University of Science and Technology Department of Geography and Environment pays non établi dans la noticeUniversité ou école supérieure
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Institute for Global Environmental Strategies Natural Resources and Ecosystem Services Area pays non établi dans la noticeOrganisation à but non lucratif
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Universiti Sains Malaysia pays non établi dans la noticeUniversité ou école supérieure
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College of Geography and Environmental Sciences pays non établi dans la noticeUniversité ou école supérieure
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School of Humanities Geography Section pays non établi dans la noticeUniversité ou école supérieure
State Key Laboratory of Efficient Utilization of Agricultural Water Resources — China Agricultural University, Zhejiang Normal University et Department of Geography and Environment — Shahjalal University of Science and Technology, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.