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1. Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

   https://doi.org/10.3390/w11051098  

   Bowes, Benjamin D.; Sadler, Jeffrey M.; Morsy, Mohamed M.; Behl, Madhur; Goodall, Jonathan L. (May 2019, Water)

   Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management. 

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2. Building Valveless Impedance Pumps From Biological Components: Progress and Challenges

   https://doi.org/10.3389/fphys.2021.770906  

   Sarvazyan, Narine (January 2022, Frontiers in physiology)

   Valveless pumping based on Liebau mechanism entails asymmetrical positioning of the compression site relative to the attachment sites of the pump’s elastic segment to the rest of the circuit. Liebau pumping is believed to play a key role during heart development and be involved in several other physiological processes. Until now studies of Liebau pump have been limited to numerical analyses, in silico modeling, experiments using non-biological elements, and a few indirect in vivo measurements. This review aims to stimulate experimental efforts to build Liebau pumps using biologically compatible materials in order to encourage further exploration of the fundamental mechanisms behind valveless pumping and its role in organ physiology. The covered topics include the biological occurrence of Liebau pumps, the main differences between them and the peristaltic flow, and the potential uses and body sites that can benefit from implantable valveless pumps based on Liebau principle. We then provide an overview of currently available tools to build such pumps and touch upon limitations imposed by the use of biological components. We also talk about the many variables that can impact Liebau pump performance, including the concept of resonant frequencies, the shape of the flowrate-frequency relationship, the flow velocity profiles, and the Womersley numbers. Lastly, the choices of materials to build valveless impedance pumps and possible modifications to increase their flow output are briefly discussed. 

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3. Spatiotemporal SWMM-LSTM surrogate modeling for efficient node-level water depth and inflow prediction in urban drainage networks

   https://doi.org/10.1017/wat.2026.10013  

   Zafarmomen, Nima; Samadi, Vidya; Borgomeo, Edoardo (January 2026, Cambridge Prisms: Water)

   Abstract Extreme weather events, combined with human-induced factors, such as expanding impervious surfaces and inadequate drainage infrastructure, are driving escalating urban flood risks worldwide. In this study, we present a novel spatiotemporal Long Short-Term Memory (LSTM)-based surrogate of the U.S. Environmental Protection Agency (EPA)’s Storm Water Management Model (SWMM) to predict maximum water depth and inflow at the asset level within urban drainage networks. The high-resolution SWMM model, encompassing the full network of conduits and manholes, was first calibrated and validated using U.S. Geological Survey (USGS) observations. The LSTM surrogate was then trained on data from 5,000 rainfall events across seven Annual Recurrence Intervals (ARIs) ranging from 1 to 100 years. The SWMM-LSTM surrogate model consistently achieves high predictive performance for both water depth and inflow, highlighting its robustness across diverse storm scenarios and ARI conditions. Hyperparameter optimization via grid search revealed task-specific configurations: larger hidden layers with moderate dropout improved water depth predictions, while deeper network architectures with minimal dropout optimized inflow forecasts. By providing rapid, computationally efficient predictions without compromising accuracy, the SWMM-LSTM surrogate offers a practical tool for real-time flood risk assessment, scenario evaluation and actionable decision-making in complex urban drainage systems. 

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4. Modification of a dual‐chamber denitrification bioreactor with a surface water pumping system

   https://doi.org/10.1002/agg2.20399  

   Hartfiel, Lindsey M.; Craig, Andrew J.; Soupir, Michelle L. (September 2023, Agrosystems, Geosciences & Environment)

   Core Ideas A subsurface drainage‐fed bioreactor was retrofitted with a supplemental surface water pumping system. Design criteria of the pumping system are presented along with challenges and future recommendations. Pumped bioreactor systems show promise for the treatment of alternative nitrate‐laden sources of water. Pumped bioreactors have the potential to remove nitrate beyond the typical drainage season. 

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5. A fast flood inundation model with groundwater interactions and hydraulic structures

   https://doi.org/10.1016/j.advwatres.2025.105057  

   Sanders, BF; Schubert, JE; Martin, EMH; Wang, S; Sukop, MC; Mach, KJ (July 2025, Advances in Water Resources)

   To efficiently predict flooding caused by intense rainfall (pluvial flooding), many physics-based flood inundation models adopt simplistic parameterizations of infiltration such as the Kostiakov, Horton, Soil Conservation Service and Green-Ampt methods. However, these methods are not explicitly dependent on soil moisture (or the groundwater table height), which is known to strongly influence the amount of runoff generated by rainfall. Models that fully couple surface and groundwater flow equations offer an alternative approach, but require larger amounts of input data and greater computational effort. Here we present a fast flood inundation model that couples two-dimensional shallow-water equations for surface flow with a zero-dimensional, time-dependent groundwater equation to capture sensitivity to groundwater. The model is also configured to account for storm drains, pumping and gates so human influences on flooding can be resolved, and is implemented with a dual-grid finite-volume scheme and with OpenACC directives for execution on graphical processing units (GPUs). With a 1.5 m resolution application across a 1,000 km area in Miami, Florida, where pluvial flooding is sensitive to depth to groundwater and simulation models that accurately reproduce observed flooding are needed to explore and plan response options, we first show that hourly water levels are predicted with a Mean Absolute Error of 8–16 cm across six canal gaging stations where flows are affected by tides, pumping, gate operations, and rainfall runoff. Second, we show high sensitivity of flooding to antecedent groundwater levels: flood extent is predicted to vary by a factor of six when initial depth to groundwater is varied between 10 and 200 cm, an amount that aligns with seasonal changes across the area. And third, we show that the model runs 30 times faster than real time (i.e., model speed = 30) using an NVIDIA V100 GPU. Furthermore, using a 3 m resolution model of Houston, Texas, we benchmark model speeds greater than 20 and 100 for domain sizes of 10,000 or 1,000 km2, respectively. The importance of model speed is discussed in the context of flood risk management and adaptation. 

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