Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction

Accurate runoff prediction is essential for effective water resource management, particularly in addressing flood control and monitoring drought conditions. However, the diverse nature of land types and varying climate conditions often complicate this task, requiring frequent adaptations to prediction models for local applications. Existing methods primarily focus on modeling for individual regions, while regional runoff prediction models cannot often learn long-term patterns, limiting their regional adaptability. To overcome this challenge, we present the temporal fusion runoff network (TFRN), a new framework designed to enhance long short-term memory (LSTM) models by enabling them to incorporate distant historical information. This innovation offers a promising framework for regional runoff prediction by enhancing model performance and minimizing computational demands. In this study, the proposed TFRN utilizes convolutional networks to extract and integrate both long-term and short-term trends from input sequences, and by merging the strengths of LSTM and Transformer architectures, TFRN achieves a thorough integration of historical data. Specifically, our method employs convolutional networks across both time and frequency domains to capture multi-scale features. Within the Transformer component, we introduce an adaptive fusion module to improve the integration of historical information. We validated the effectiveness of our model using two extensive hydrological datasets for a 7-day runoff prediction task. The results underscore the superiority of our approach, demonstrating its advantages over several leading methods. The source code is available at https://github.com/redtea-code/TFRN.