A new integrated prediction method of river level based on spatiotemporal correlation

As an essential hydrological variable, the river water level is vital in research fields such as agriculture. This study proposes a new multi-step river water level prediction method based on spatiotemporal correlations ST-ESMC-AE-XGBoost. First, the temporal correlation prediction models are established by conducting Pearson correlation analysis for different hydrological monitoring points: T-Models; secondly, ChebNet, a graph neural networks (GNN) based on the spatial topology of each hydrological monitoring point, is established from the perspective of spatial correlation; finally, AE-XGBoost is used to reconstruct spatiotemporal features and perform integrated prediction. This study uses the research object of the nine hydrological monitoring stations in the Yangtze River Basin. The experiments show that: (1) classic methods such as extreme learning machines (ELM) still have stable application value in hydrological prediction; (2) T-Models can further improve the performance of single models such as support vector machine (SVM), ELM, and multi-layer perceptrons (MLP) in multi-step water level prediction; (3) using the ChebNet can learn beneficial information from the geospatial structure of hydrological monitoring points, further improve the prediction effect of the spatiotemporal association integration model; (4) the multi-step water level prediction method ST-ESMC-AE-XGBoost proposed in this study has the highest prediction accuracy and the best generalization performance among all compared models.