Employing Artificial Intelligence to Predict δ¹⁸O and δ²H Isotope Ratios in Precipitation in Iraq under Changing Climate Patterns

Understanding precipitation dynamics in arid regions such as Iraq is of paramount importance in hydrological studies, representing a key approach for water resources management and climate change adaptation. This study aims to develop a mathematical model to predict isotopic values using artificial intelligence techniques. Stable isotope data for oxygen (δ¹⁸O) and deuterium (δ²H) in rainfall were collected from 32 stations distributed across Iraq over a 14-year period (2010–2024). The dataset also included meteorological parameters for these stations including, precipitation intensity, temperature, relative humidity, and calculated station elevation. Several machine learning algorithms (i.e., SVM, GBR, ANN, CatBoost, XGBoost, and RF) were employed to compare the predicted isotopic values and the actual readings influenced by rainfall characteristics and patterns. The results demonstrated that the RF model achieved superior predictive performance, with a calibration coefficient (R²) of 0.89 in the testing set, indicating strong predictive capability. This model also recorded the lowest mean absolute error (MAE) of 1.39 and the lowest root mean square error (RMSE) of 3.5 compared to the other algorithms, reflecting improved predictive accuracy. These findings confirm the effectiveness of integrating machine learning, particularly the RF approach, in enhancing the modeling of isotopic signature predictions in environmental studies. Furthermore, they highlight the potential of AI-based models as powerful tools for reconstructing isotopic data for past periods, which can significantly support climate change analysis and water management planning in arid regions.