Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models - Pin-Chun Huang; Kwan Tun Lee (Geomatics, Natural Hazards & Risk, 2025)
The primary merit of data-driven-based runoff models is their capability to handle various inputs, including hydrological, land use, and geographical data, allowing for flexibility regarding different environmental conditions and landscapes. Physics-based models provide a comprehensive framework for understanding runoff processes, offering physical realism and transferability advantages. In contrast, they may require more expertise and complicated numerical operations compared to data-driven models. The present study aims to improve the predictive capability of data-driven models by including the advantages of physics-based models in the model’s structure and preprocessing input features. To achieve this goal, associated environmental factors adopted in theoretical models, having more rigorous physical interpretation for runoff predictions, are thoroughly examined, especially for the features associated with topographic descriptors. The topological distribution inherent in the input data space is analyzed to improve predictive accuracy. The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. The main contribution of this study is to establish a robust runoff model that retains the original superiority of the data-driven model while extending its capability to capture hydrological processes and underlying physical influences in predicting hydrological responses from river basins.