Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting

Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation of hydro-meteorological variables is fundamentally challenging for artificial neural networks. This challenge becomes even more significant when the complexity involved introduces systematic biases and time-lag phenomena in flood forecasts. For the first time, this study proposed a Spatiotemporal Hetero Graph-based Long Short-term Memory (SHG-LSTM) model for multi-step-ahead flood forecasting. The case study focused on the Jianxi basin in China. 25,341 hydro-meteorological data, with a temporal resolution of three hours, collected during flood events were divided into training and test datasets for model construction purpose. The model was fed with 3-h streamflow and precipitation data from 23 gauge stations, covering a time span of the preceding 21 h, for generating flood forecasts at 1 up to 7 horizons. To make a comparative analysis, both LSTM and the Spatiotemporal Graph Convolutional Network (S-GCN) were constructed. This study conducted multiple rounds of model training with varying initial parameters to assess the accuracy, stability, and reliability of the LSTM, S-GCN, and SHG-LSTM models. The results demonstrated that the SHG-LSTM model outperformed LSTM and S-GCN models, with an average reduction in the volume error (VE) of 6.5% and 11.1%, respectively, a decrease in the Mean Absolute Error (MAE) of 6.7% and 8.1%, respectively, and a reduction in the Root Mean Square Error (RMSE) of 5.0% and 12.9%, respectively. Furthermore, the SHG-LSTM model not only could efficiently overcome the under-prediction bottleneck, but also could largely mitigate the time-lag phenomenon in flood forecasts, even during the testing stages. These findings indicate that the proposed SHG-LSTM model can provide a general framework for modelling the spatial heterogeneity and correlation of hydro-meteorological variables and achieve accurate and reliable flood forecasts, thereby enhancing the model's applicability in flood prevention platforms and systems.