A new method for predicting precipitation δ18O distribution based on deep learning and spatio-temporal clustering

Abstract

ABSTRACT Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet forecasting its spatio-temporal distribution remains challenging due to complex climate interactions and extreme events. We developed a method combining spatio-temporal clustering and deep learning neural networks to improve multi-site, multi-year precipitation δ18O predictions. Using a comprehensive dataset from 33 German sites (1978–2012), our model considers precipitation δ18O and its controlling factors, including precipitation and temperature distribution. We applied the K-means ++ method for classification and divided data into training and prediction sets. The convolutional neural network (CNN) model extracted spatial features, while the bi-directional long short-term memory (Bi-LSTM) model focused on temporal features. Spatio-temporal clustering using K-means ++ improved forecast accuracy and reduced errors. This study highlights the potential of deep learning and clustering techniques for forecasting complex spatio-temporal data and offers insights for future research on isotope distributions.