Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

Debris content plays an important role in controlling erosion capacity and sedimentation characteristics during outburst floods. Numerical models should incorporate sediments in hazard analyses to obtain more accurate assessments of outburst flood magnitudes and downstream behaviors. In this paper, we propose a novel water–sediment mixture flow model to reconstruct the ancient outburst floods at Xuelongnang and demonstrate the performance of this model through comparisons with field evidence. The simulated outburst flood reaches a maximum breach discharge of 10,697.35 m³/s and a peak sediment discharge of 65.29 m³/s, traveling downstream for 87 km within 5.43 h. Based on simulations of riverbed changes, inundation depth, velocity, shear stress, and Froude number, our findings suggest that topographic controls influence hydraulic patterns, which subsequently affect erosional and depositional processes and contribute to landscape evolution. During the downstream propagation of the outburst flood in narrowed valley sections, simulated sediment-simulated deposition occurs downstream while erosion occurs upstream, coinciding with the maximum inundation depths attributed to hydraulic jump phenomena. We also discuss the formation processes of the outburst deposits, identifying areas of greatest channel aggradation. Calculated bed shear stress suggests that sediment transport by the flood deposits on the riverbed decreases as the flood stage wanes, forming the rhythmite-interbedded structures observed in field investigations. This work provides a viable and promising approach to understanding hydro-sediment-morphodynamic processes in flood pathways and the erosional and depositional features left by outburst floods, supporting modern outburst flood hazard prevention and mitigation.