Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities. Subsequently, we establish an initial regression model using the TrAdaBoost algorithm based on the hydrologic data from the selected watershed stations. Finally, we refine the initial model by incorporating multiple spatiotemporal views, employing semi-supervised learning to create the STH-Trans model. The results of our experiments underscore the efficiency of the STH-Trans model in predicting runoff for ungauged basins. This innovation leads to a substantial increase in model accuracy ranging from 7.9% to 30% compared to various conventional methods. The model not only offers data support for water resource management, flood mitigation, and disaster relief efforts, but also provides decision support for hydrologists.
