Application of machine learning algorithms for groundwater level prediction in the Najafabad plain.

ABSTRACT: Accurate groundwater level prediction is essential for sustainable water management in arid and semi-arid regions. This study evaluated three machine learning models-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM)-to forecast groundwater levels across five hydrogeological zones of the Najafabad Plain, Iran. Input variables included climatic (precipitation, temperature), hydrological (previous groundwater level), and anthropogenic (irrigation and groundwater abstraction) factors. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), Willmott's index (WI), and percent bias (PBIAS). Among the algorithms, XGBoost showed the best predictive skill, with mean testing results o

SUBMITTER: Davari S 

PROVIDER: S-EPMC12820121 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature