Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
This addresses water resource management challenges in regions with limited streamflow data, though it appears incremental as an adaptation of existing domain adaptation techniques to this specific problem.
The paper tackles streamflow forecasting in data-sparse regions by proposing an attention-based domain adaptation method that leverages data-rich source domains, achieving improved performance for 24-hour lead-time predictions compared to baseline models.
Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction in a data-constrained target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24hr lead-time streamflow forecasting.