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Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-24571-3openalex-snapshot
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
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Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model
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A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling
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Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
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Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements
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Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA
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Newton–Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems
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Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources
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Coupling groundwater and land surface processes: Idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes
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Evapotranspiration depletes groundwater under warming over the contiguous United States
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No progress on diversity in 40 years
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Deep learning for multi-year ENSO forecasts
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Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: transient, stochastic simulations
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Resolution matters when modeling climate change in headwaters of the Colorado River
inferredcites →https://doi.org/10.1098/rsta.2020.0098openalex-snapshot
Machine learning for weather and climate are worlds apart
inferredcites →https://doi.org/10.1126/sciadv.aav4574openalex-snapshot
Simulating the sensitivity of evapotranspiration and streamflow to large-scale groundwater depletion
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Machine learning: Trends, perspectives, and prospects
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Race and gender differences in how sense of belonging influences decisions to major in STEM
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A Parallel Multigrid Preconditioned Conjugate Gradient Algorithm for Groundwater Flow Simulations
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Inside the Double Bind: A Synthesis of Empirical Research on Undergraduate and Graduate Women of Color in Science, Technology, Engineering, and Mathematics
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Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks
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Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model
inferredcites →https://doi.org/10.5194/hess-23-1931-2019openalex-snapshot
Evaluating the relative importance of precipitation, temperature and land-cover change in the hydrologic response to extreme meteorological drought conditions over the North American High Plains
inferredcites →https://doi.org/10.5194/hess-26-3377-2022openalex-snapshot
Deep learning rainfall–runoff predictions of extreme events
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Hydroecolstm: A Python Package with Graphical User Interface for Hydro-Ecological Modelling with Long Short-Term Memory Neural Network
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Ensemble learning chlorophyll-a concentration prediction in lakes based on multisource heterogeneous data
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HydroSignal: open-source internet of things information communication platform for hydrological education and outreach
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2025.102994openalex-snapshot
HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2023.129515openalex-snapshot
Recent advances in integrated hydrologic models: Integration of new domains
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2023.130240openalex-snapshot
Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams
inferred← citeshttps://doi.org/10.1029/2025wr041736openalex-snapshot
The Current State of Undergraduate Hydrology Courses in North America: A Path Forward
inferred← citeshttps://doi.org/10.1080/03098265.2024.2316704openalex-snapshot
How you teach changes who you reach: understanding the effect of teaching modality on engagement, interest, and learning in hydrology
inferred← citeshttps://doi.org/10.1109/educon52537.2022.9766555openalex-snapshot
Internet of Things Meets Machine Learning: A Water Usage Alert Example
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1643745/v1openalex-snapshot
Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams
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Peningkatan Literasi Sumber Daya Air Tanah Menggunakan Media Interaktif Berbasis Android
inferred← citeshttps://doi.org/10.5194/egusphere-2022-967openalex-snapshot
How you teach changes who you reach: understanding the effect of teaching modality on student engagement, content interest, and learning in undergraduate hydrology
inferred← citeshttps://doi.org/10.5194/egusphere-2022-967-rc1openalex-snapshot
Comment on egusphere-2022-967
inferred← citeshttps://doi.org/10.5194/egusphere-2022-967-rc2openalex-snapshot
Comment on egusphere-2022-967
inferred← citeshttps://doi.org/10.5194/egusphere-2022-967-rc3openalex-snapshot
Comment on egusphere-2022-967
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