A deep time-series water level prediction framework based on internal and external influencing factors: Targeting agricultural irrigation reservoirs

The fluctuation of water levels in agricultural irrigation reservoirs is extremely important for effective planning and allocation of irrigation water resources by agricultural producers, and can be predicted using time-series neural networks. However, existing time-series models fail to capture the spatial characteristics in the input data. Furthermore, there is a lack of comprehensive analysis and extraction of internal and external factors that effectively influence water level changes in the dataset prior to model training, and the methods for determining optimal window values lack interpretability and scientific validity. Therefore, we propose a deep time-series water level prediction framework based on internal and external influencing factors. The framework includes three key components: a Data Preprocessing method based on Internal and External influencing factors (DPIE), a Time-series Window Parameter Optimization method based on Fast Fourier Transform (FTOM), and a CNN-SSA-GRU deep time-series model (CSG), which improve predictive performance in terms of input data quality, optimal time-series window value, and model structure respectively. Comparative experiments and ablation studies conducted on the test set demonstrate that the collaborative combination of these three components significantly enhances the accuracy of water level prediction. Multi-step prediction comparison experiments prove the framework's significant advantage in predicting water levels for multiple days ahead. Additionally, this paper uses the SHAP tool to analyze the interpretability of the decision-making process in the model's predictive results. In summary, this framework can assist agricultural producers in more effectively planning and allocating irrigation water resources and also provides a thought exploration and reference for water level prediction problems in other aquatic environments.