Exploring Long-term Monthly Prediction of Precipitation Isotopes over Southeast Asia: A Comparative Analysis of Machine-Learning Models

Abstract. Using stable isotope methods is essential for studying tropical hydrology and climatology. The purpose of this research was to investigate the influence of large-scale climate modes (teleconnection indices) and local meteorological parameters on the stable isotope contents in six different stations, including Bangkok, Kuala Lumpur, Jakarta, Kota Bharu, Jayapura, and Singapore in Southeast Asia. To achieve this goal, several machine learning (ML) techniques were employed, such as shallow neural network (SNN), deep neural network (DNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). XGBoost demonstrated the highest accuracy across the majority of studied stations, with a R² = 0.91, VNS=0.90, AIC= 405, BIC=410, and RMSE = 0.76. Additionally, DNN exhibited superior accuracy in specific cases, achieving a R² = 0.87, VNS=0.87, AIC = 445, BIC = 460, and RMSE = 1.10. Furthermore, a bootstrap analysis was conducted to assess the uncertainty of the simulated data in each station. The results of this analysis demonstrated acceptable accuracy, as the majority of simulated data points fell within the 95 % confidence intervals. Finally, stable isotope contents in precipitation were forecasted for one year using Vector Autoregression (VAR) and ML techniques. This study underscores the efficacy of ML techniques in both simulating and forecasting stable isotope contents with high precision. The inclusion of specific accuracy metrics strengthens the validity of claims in this study and provides a clearer picture of the quantitative outcomes of this research.