Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling

Abstract

Jilin Province, a key agricultural hub in Northeast China, has long been impacted by climate change, with drought disasters significantly affecting its agricultural output and ecological environment. Accurate drought prediction is essential for the effective utilization of water resources and agricultural production. This study proposes a novel drought prediction model that utilizes the SSA-VMD (Sparrow Search Algorithm Optimized Variational Mode Decomposition) technique to decompose meteorological data, followed by the reconstruction of the decomposed components using four entropy algorithms, including approximate entropy. The model integrates ARIMA (AutoRegressive Integrated Moving Average) and BiLSTM (Bidirectional Long Short-Term Memory network) for forecasting the reconstructed components, subsequently combining their outputs. In terms of spatio-temporal data, the study employs prediction models including the spatio-temporal cube, spatio-temporal hotspot analysis integrated with empirical kriging, and local outlier analysis to examine spatial distribution. The model’s predictive performance is validated from three perspectives: statistical characteristics of the indicators, comparison between predicted and observed values through prediction curve plots, and box plots. The results demonstrate that the combined SSA-VMD-ARIMA-BiLSTM model significantly enhances prediction accuracy compared to single models, as exemplified by its application in Changchun City. The model achieved an R2 of 0.938 and a root mean square error (RMSE) of 0.047 in drought prediction, outperforming the single ARIMA model (R2: 0.636, RMSE: 0.709) and the BiLSTM model (R2: 0.514, RMSE: 0.901). Additionally, across the entire province, the model’s R2, MAE, and RMSE are 0.82, 0.15, and 0.083, respectively, suggesting that the model exhibits not only high prediction accuracy but also a degree of generalizability. Furthermore, the results from the spatio-temporal cube, spatio-temporal hotspot analysis, and local outlier analysis demonstrate the method’s high accuracy and stability in predicting both short-term and long-term droughts. Particularly in short-term drought prediction, the model effectively captures the spatio-temporal distribution characteristics of short-term meteorological droughts. This study offers new methodological support for enhancing the early warning capabilities of drought risk in Jilin Province, providing a robust foundation for addressing the challenges posed by climate change. The findings not only address certain shortcomings in current drought prediction research but also introduce new methodologies and perspectives for future studies.