Exploring stable isotope patterns in monthly precipitation across Southeast Asia using contemporary deep learning models and SHapley Additive exPlanations (SHAP) techniques

Abstract

Stable isotopes are crucial for understanding water cycles and climate dynamics, particularly in tropical regions. However, establishing and maintaining precipitation sampling stations in Southeast Asia is challenging due to high costs and logistical issues. Consequently, many areas in this region have limited or no sampling stations with adequate stable isotope data. To address this problem, developing models that simulate stable isotope contents using machine learning (ML) techniques, especially deep learning, is a promising solution. In this study, the influence of large-scale climate modes (teleconnection indices) and local meteorological parameters on the stable isotope contents of precipitation was examined across six key stations in Southeast Asia, including Bangkok, Kuala Lumpur, Jakarta, Kota Bharu, Jayapura, and Singapore. A deep neural network (DNN) model was applied for simulation, and its performance was compared with a partial least squares regression (PLSR) model using various evaluation metrics. The DNN consistently demonstrated superior accuracy across all studied stations, highlighting the efficacy of DNNs, in accurately simulating stable isotope contents in tropical precipitation. The importance ranking derived from the SHapley Additive exPlanations (SHAP) technique aligns perfectly with the results obtained from the DNN importance function. In addition, the SHAP summary plot highlights the contributions of key features, such as precipitation and potential evaporation, to the model's predictions. The dependence plots further illustrate the relationship between these features and the predicted response, revealing nonlinear interactions that influence model behaviour. This research provides new insights into the complex interactions between large-scale climate drivers and local weather patterns, advancing the use of ML for isotope-based climate studies. The techniques used in this study offer a framework for applying ML to isotope analysis in tropical climates and can be extended to similar regions worldwide.