{"total":26,"rows":[{"id":"2203.14827","title":"Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy","created":"2022-03-28","cited_by_count":21,"area":null},{"id":"2109.06429","title":"Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology","created":"2021-09-14","cited_by_count":15,"area":null},{"id":"2111.02780","title":"Flood forecasting with machine learning models in an operational framework","created":"2021-11-04","cited_by_count":15,"area":null},{"id":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","cited_by_count":11,"area":null},{"id":"2101.01876","title":"The data synergy effects of time-series deep learning models in hydrology","created":"2021-01-06","cited_by_count":10,"area":null},{"id":"2306.12384","title":"Probing the limit of hydrologic predictability with the Transformer network","created":"2023-06-21","cited_by_count":9,"area":null},{"id":"2310.08644","title":"A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems","created":"2023-10-12","cited_by_count":7,"area":null},{"id":"2104.11009","title":"Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes","created":"2021-04-22","cited_by_count":6,"area":null},{"id":"2401.14521","title":"Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron","created":"2024-01-25","cited_by_count":5,"area":null},{"id":"2101.04423","title":"Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy","created":"2021-01-12","cited_by_count":4,"area":null},{"id":"2301.04027","title":"Differentiable modeling to unify machine learning and physical models and advance Geosciences","created":"2023-01-10","cited_by_count":4,"area":null},{"id":"2305.12335","title":"Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence","created":"2023-05-21","cited_by_count":4,"area":null},{"id":"2412.04845","title":"Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics","created":"2024-12-06","cited_by_count":4,"area":null},{"id":"2201.01869","title":"Earthquake Nowcasting with Deep Learning","created":"2021-12-18","cited_by_count":3,"area":null},{"id":"2601.01558","title":"Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings","created":"2026-01-04","cited_by_count":3,"area":null},{"id":"2406.07484","title":"Towards Generalized Hydrological Forecasting using Transformer Models for 120-Hour Streamflow Prediction","created":"2024-06-11","cited_by_count":1,"area":null},{"id":"2607.03217","title":"Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models","created":"2026-07-03","cited_by_count":1,"area":null},{"id":"2106.09547","title":"Machine Learning for Postprocessing Ensemble Streamflow Forecasts","created":"2021-06-15","cited_by_count":0,"area":null},{"id":"2404.14212","title":"Toward Routing River Water in Land Surface Models with Recurrent Neural Networks","created":"2024-04-22","cited_by_count":0,"area":null},{"id":"2407.20152","title":"Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systems","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2502.16444","title":"Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models","created":"2025-02-23","cited_by_count":0,"area":null},{"id":"2504.10707","title":"Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning","created":"2025-04-14","cited_by_count":0,"area":null},{"id":"2510.03744","title":"HydroFusion-LMF: Semi-Supervised Multi-Network Fusion with Large-Model Adaptation for Long-Term Daily Runoff Forecasting","created":"2025-10-04","cited_by_count":0,"area":null},{"id":"2510.10799","title":"Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage","created":"2025-10-12","cited_by_count":0,"area":null},{"id":"2603.25093","title":"Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2604.23045","title":"A Differentiable Framework for Global Circulation Model Precipitation Bias Correction","created":"2026-04-24","cited_by_count":0,"area":null}],"next_cursor":null}