Zhao Qun, Zhu Yuelong, Shi Yanfeng, Li Rui, Zheng Xiangtian, Zhou Xudong
School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China.
College of Computer and Information, Hohai University, Nanjing, Jiangsu, China.
PLoS One. 2025 Jan 10;20(1):e0313535. doi: 10.1371/journal.pone.0313535. eCollection 2025.
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.
数据匮乏流域的水文预测通常依赖于参数移植法，该方法因依赖专家输入而导致劳动力成本高昂。为解决这些问题，我们提出了一种名为STH-Trans的新型水文预测模型，它利用多个时空视图来增强其预测能力。首先，我们利用现有的地理和地形指标来识别和选择表现出相似性的流域。随后，我们基于所选流域站点的水文数据，使用TrAdaBoost算法建立初始回归模型。最后，我们通过纳入多个时空视图来优化初始模型，采用半监督学习创建STH-Trans模型。我们的实验结果强调了STH-Trans模型在预测数据匮乏流域径流方面的效率。与各种传统方法相比，这一创新使模型准确率大幅提高，增幅在7.9%至30%之间。该模型不仅为水资源管理、防洪和救灾工作提供数据支持，还为水文工作者提供决策支持。
