Accurate river velocity prediction plays a vital role in water resource management and hydraulic engineering. Seasonal variations and weather conditions introduce complex temporal patterns, making prediction a challenging task. Traditional methods struggle to account for both short-term fluctuations and long-term trends, and they fail to fully exploit temporal information. To overcome these limitations, we propose SeekRiver, a prediction model that integrates both long- and short-term features. We first transform time features into low-dimensional vectors through an embedding layer, enhancing their temporal representation. The model uses two LSTM layers to separately capture short-term and long-term dependencies. An attention mechanism is employed to emphasize crucial features while minimizing the impact of irrelevant ones. A weighted fusion strategy then combines multi-scale features to improve prediction accuracy. Extensive experiments, including comparisons with baseline models and ablation studies, validate SeekRiver’s effectiveness. The model significantly outperforms traditional methods, with the attention mechanism and short-term feature module contributing the most to the performance improvement, which offers a robust and efficient solution for river velocity prediction.

The rise of global data-driven applications has made geo-distributed hybrid transactional and analytical processing (HTAP) databases increasingly desirable. Existing distributed HTAP systems provide users with good performance on both transactions and analytical queries, and this good performance is scalable across a large number of data nodes. Unfortunately, these systems either provide weak consistency or incur bad data freshness when deployed geographically. In this paper, we present P erseus , a scalable HTAP database that enforces strong consistency for both transactions and analytical queries. To handle consistency efficiently, P erseus augments the classical dependency graph in concurrency control protocols to explicitly record the versions of data and their complete dependencies, implying which data needs to be read together in a snapshot. To minimize data staleness on analytical queries (another important goal of HTAP), P erseus further introduces a new dynamic snapshot algorithm that chooses updates selectively. Extensive evaluation results show that, compared to the HTAP databases with even weaker consistency, P erseus achieves up to 90% lower visibility delay, a metric of data freshness, capturing the time interval during which transactional updates are committed to the database and can be visible to analytical queries. Besides, Perseus is scalable across many nodes and robust to network instability.