Comparative Evaluation of Physics-Informed and Data-Driven Neural Networks for Reservoir Water Balance Simulation During Hydrological Extremes

Assessing Physics-Informed Neural Networks in Streamflow Predictions for Tropical Monsoonal Basins in Sri Lanka

Globally, escalating floods and droughts demand accurate streamflow prediction for effective water management and early warning. Yet, this remains challenging in tropical monsoon regions. Traditional process-based models (e.g., HEC-HMS) underperform given extensive data needs, while purely data-driven models (e.g., LST...

Physics-informed LSTMs improve streamflow prediction when storage-discharge dynamics are temporally resolved

The results show that the value of physics-informed learning depends on whether the constrained physical process is resolved at the modelling timestep, and suggest that temporal resolution should be considered explicitly when designing and evaluating physics-informed hydrological models.

Liquid Time-Constant Networks for Water Level Forecasting in Urban Drainage: Adaptive Time-Scale Modeling of Hydrological Dynamics

Liquid Time-Constant networks (LNNs), recurrent models with adaptive, input-dependent time constants, were evaluated for water level prediction in an urban drainage system. The architecture was assessed on a ten-year measurement dataset from the Bellinge catchment and benchmarked against gated recurrent unit (GRU), lon...

Riverine Flood Forecasting Using Advanced Deep Learning Approaches

Analysis of riverine flood forecasting models revealed that PatchTST outperformed the other models during moderate‐flow regimes while falling behind during extreme flooding events, and sensitivity analysis results indicated that PatchTST was slightly more sensitive to the selected training data features.

Predicting Thermal Structure in Large Reservoirs Under Climate Change Using Machine Learning

PINN-Sed: Interconnected Physics-Informed Machine Learning Model for High Resolution Suspended Sediment Transport in River Network

There is growing demand for river-network models that can estimate sediment transport at fine temporal resolution. Although coupling physically based sediment modules with hydrological and hydrodynamic models is a logical path forward, these approaches require intensive calibration and substantial computing power, li...