The study presents a daily runoff prediction model for the Ajichay River in northwest Iran, employing an ensemble of five Long Short-Term Memory (LSTM) units trained on varying time intervals (2–6 days). Each LSTM is fine‑tuned with Particle Swarm Optimization to enhance accuracy, and the ensemble’s performance on 2017–2018 data shows $R^2$ values between 74.95% and 91.42%, with RMSE and MSE also reported. Feature‑importance analyses identify key variables influencing runoff variations.
arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.
arXiv:2606. 02791v1 Announce Type: new Abstract: Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes.
arXiv:2608. 01180v1 Announce Type: new Abstract: Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity.
arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
The study presents a deployment‑aware framework for forecasting spring discharge and groundwater levels in the Edwards Aquifer over 1‑12 week horizons using 79 years of hydroclimatic data. Five machine‑learning families—extreme gradient boosting, extremely randomized trees, LSTM, CNN, and Transformers—were compared, with extreme gradient boosting consistently delivering the highest reliability (R² ≥ 0.94) and strong agreement with operational drought thresholds. The validated models were integrated into a five‑agent operational architecture that automates data acquisition, model selection, prediction, threshold monitoring, verification, literature retrieval, and reporting.
arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.
The paper introduces the Mass‑Conserving Perceptron (MCP), a physics‑aware AI framework that enforces conservation laws while learning hydrological process relationships from data. By progressively adding physically meaningful components—such as bounded soil storage, state‑dependent conductivity, variable porosity, infiltration capacity, surface ponding, vertical drainage, and nonlinear water‑table dynamics—to a single MCP storage unit, the authors demonstrate that predictive skill for daily streamflow improves across 15 U.S. catchments. The study finds that the impact of each process representation varies with hydroclimate, with vertical drainage boosting performance in arid and snow‑dominated basins but hindering it in rainfall‑dominated ones, while surface ponding has minimal effect; the best MCP configurations rival LSTM benchmarks while retaining explicit physical interpretability.
KNOU‑Flood extends the open AIFL recipe for global streamflow forecasting by retaining a single‑layer LSTM and two‑stage transfer learning while adding a longer 424‑day input window, lead‑aware forecast inputs, and GraphCast as an additional forecast source. The model also introduces a latent‑noise ensemble trained with the fair CRPS, achieving a median KGE′ of 0.65 at lead time 1 and 0.48 at lead time 10 across 2,099 test basins, outperforming AIFL and a 1,024‑unit baseline. The probabilistic head, GraphCast channels, longer window, and mixed fine‑tuning collectively reduce lead‑time decay, improve calibration, and enhance detection of multi‑year floods.
arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.
arXiv:2510. 02605v2 Announce Type: replace Abstract: While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding.
arXiv:2602.22293v2 Announce Type: replace Abstract: Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning...
The study investigates whether a physically grounded, low‑dimensional catchment model can rival the predictive accuracy of neural networks. By applying three fundamental hydrological laws—conservation, dissipation, and aggregation—to a lumped model, the authors develop CaRT and its refined version CaRT+, which uses 14 states and 35 parameters per catchment. Across 602 British, 593 US, and 505 Australian gauged catchments, CaRT+ achieves median Nash‑Sutcliffe efficiencies comparable to state‑of‑the‑art LSTM models, demonstrating that process‑based laws can match deep‑learning performance.