The following pages link to Alison Appling, PhD (Q44528):
Displayed 48 items.
- Overcoming equifinality: Leveraging long time series for stream metabolism estimation (Q145207) (← links)
- The metabolic regimes of flowing waters (Q145278) (← links)
- Graph-based reinforcement learning for active learning in real time: An application in modeling river networks (Q146037) (← links)
- Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data (Q146517) (← links)
- Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions (Q150101) (← links)
- Machine learning for understanding inland water quantity, quality, and ecology (Q150299) (← links)
- Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature (Q150301) (← links)
- Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling (Q150546) (← links)
- Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil (Q253515) (← links)
- Multi-task deep learning of daily streamflow and water temperature (Q253766) (← links)
- Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations (Q256026) (← links)
- Stream temperature prediction in a shifting environment: The influence of deep learning architecture (Q257873) (← links)
- Partial differential equation driven dynamic graph networks for predicting stream water temperature (Q258651) (← links)
- Long-term change in metabolism phenology in north temperate lakes (Q267172) (← links)
- Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory (Q269962) (← links)
- Physics-guided machine learning from simulation data: An application in modeling lake and river systems (Q273872) (← links)
- Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes (Q274617) (← links)
- Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins (Q277126) (← links)
- Detecting signals of large‐scale climate phenomena in discharge and nutrient loads in the Mississippi‐Atchafalaya River Basin (Q277557) (← links)
- Identifying structural priors in a hybrid differentiable model for stream water temperature modeling (Q282036) (← links)
- Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams (Q283899) (← links)
- Light and flow regimes regulate the metabolism of rivers (Q285538) (← links)
- Physics-guided recurrent graph model for predicting flow and temperature in river networks (Q287501) (← links)
- The metabolic regimes of 356 rivers in the United States (Q289730) (← links)
- Deep learning for water quality (Q290139) (← links)
- Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning (Q292218) (← links)
- Heterogeneous stream-reservoir graph networks with data assimilation (Q292369) (← links)
- Differentiable modelling to unify machine learning and physical models for geosciences (Q292385) (← links)
- Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost (Q299006) (← links)
- Can machine learning accelerate process understanding and decision-relevant predictions of river water quality? (Q307225) (← links)
- Process-guided deep learning predictions of lake water temperature (Q308420) (← links)
- Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction (Q309734) (← links)
- Data release: Process-guided deep learning predictions of lake water temperature (Q318589) (← links)
- Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions (Q319812) (← links)
- Data to support water quality modeling efforts in the Delaware River Basin (Q319838) (← links)
- Identifying structural priors in a hybrid differentiable model for stream water temperature modeling at 415 U.S. basin outlets, 2010-2016 (Q319841) (← links)
- A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay (Q322893) (← links)
- Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin (Q323561) (← links)
- Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin (Q324344) (← links)
- Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release) (Q324462) (← links)
- Model predictions for heterogeneous stream-reservoir graph networks with data assimilation (Q324991) (← links)
- Stream temperature predictions in the Delaware River Basin using pseudo-prospective learning and physical simulations (Q325536) (← links)
- Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022) (Q326264) (← links)
- Metabolism estimates for 356 U.S. rivers (2007-2017) (Q327737) (← links)
- Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes (Q329682) (← links)
- Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins (Q336142) (← links)
- Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data (Q336158) (← links)
- streamMetabolizer - Models for Estimating Aquatic Photosynthesis and Respiration (Q336628) (← links)