Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches

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dc.authoridKARAKOYUN, ERKAN/0000-0003-2821-9103
dc.authoridSIMSEK, OGUZ/0000-0001-6324-0229
dc.authoridYavuz, Veysel S./0000-0002-5867-7677
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorKartal, Veysi
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorKarakoyun, Erkan
dc.contributor.authorSimsek, Oguz
dc.contributor.authorYavuz, Veysel Suleyman
dc.contributor.authorAriman, Sema
dc.date.accessioned2024-12-24T19:27:32Z
dc.date.available2024-12-24T19:27:32Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThis study aims to predict groundwater drought-based meteorological drought index using machine learning instead of traditional approaches. Groundwater drought (GWD) was predicted using machine learning methodologies such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Least Squares Boosting Tree (LSBT), Generalized Linear Regression (GLR) and kNearest Neighbours (KNN). In addition, monthly, seasonal, and annual drought indices such as the Standardised Precipitation-Evapotranspiration Index (SPEI), China Z Index (CZI), Standardised Precipitation Index (SPI), ZScore Index (ZSI), Decile Index (DI), Percent of Normal Index (PNI) and Rainfall Anomaly Index (RAI) were used to analyse the drought of groundwater. These traditional drought indices were modified for the assessment of groundwater drought. Moreover, groundwater drought was predicted based on the hydro-meteorological parameters (temperature, relative humidity, wind speed, rainfall, groundwater level). The applied models' performances were evaluated via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), R-squared (R2), Mean Bias Error (MBE), Bias Factor, and Variance Account Factor (VAF). Linear SVM is generally the best model for predicting GWD, while GLR is the second-best performing model. The KNN algorithm obtained the weakest performances. Although all types of drought and wet categories were observed, normal drought occurred more than in the other drought and wet categories. This study can contribute to the assessment of groundwater drought in regions where there is no groundwater station.
dc.identifier.doi10.1016/j.pce.2024.103757
dc.identifier.issn1474-7065
dc.identifier.issn1873-5193
dc.identifier.scopus2-s2.0-85206160810
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.pce.2024.103757
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6689
dc.identifier.volume136
dc.identifier.wosWOS:001335585300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofPhysics and Chemistry of The Earth
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectGroundwater level
dc.subjectGroundwater drought
dc.subjectDrought indices
dc.subjectPrediction
dc.subjectMachine learning
dc.subjectCognitive approaches
dc.titlePrediction of groundwater drought based on hydro-meteorological insights via machine learning approaches
dc.typeArticle

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