Abstract The increasing frequency and intensity of flooding events, driven by climate change and land-use modifications, call for the development of more advanced prediction tools to support early warning systems and disaster mitigation strategies. This chapter explores the use of multiple Neural Networks for flood prediction, focusing on their application in two complex and hydrologically challenging gauging stationgaging stations in the US: the Satilla River near Waycross, Georgia, and the coastal area of Socastee, South Carolina. We examined three state-of-the-art neural networks with different algorithmic structures including N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), a residual-based model; LSTM (Long Short-Term Memory), a recurrent neural network optimized for capturing sequential dependencies; and PatchTST, a transformer-based architecture utilizing self-attention and patch embedding strategies. All models were trained using multi-year hydrometeorological time series data (2007-2022) and evaluated on an independent testing set (2022-2024) across multiple prediction horizons (1-, 3-, 6-, and 12-hours). Among multiple models, N-HiTS consistently outperformed LSTM and PatchTST in both flood-prone settings. N-HiTS demonstrated superior accuracy, especially under complex tidal conditions, due to its hierarchical structure and multi-scale feature representation. PatchTST performed competitively in stable hydrological regimes, while LSTM struggled with long-term dependencies and dynamical shift in hydrological behaviors. These results emphasize the effectiveness of N-HiTS in capturing flood dynamics across multiple horizons enhancing flood prediction reliability across multiple temporal and spatial scales.
Saberian et al. (2025) studied this question.