Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Abstract

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.