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1. Scalable Riemann Solvers with the Discontinuous Galerkin Method for Hyperbolic Network Simulation

   https://doi.org/10.1145/3592979.3593421  

   Hamilton, Aidan; Qiu, Jing-Mei; Zhang, Hong (July 2023, Platform for Advanced Scientific Computing (PASC))

   We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others. 

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2. Fair Graph Learning Using Constraint-Aware Priority Adjustment and Graph Masking in River Networks

   https://doi.org/10.1609/aaai.v38i20.30212  

   He, Erhu; Xie, Yiqun; Sun, Alexander; Zwart, Jacob; Yang, Jie; Jin, Zhenong; Wang, Yang; Karimi, Hassan; Jia, Xiaowei (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated knowledge from multiple rivers to predict water flow and temperature within large-scale stream networks. Additionally, we introduce node-specific graph masks for information aggregation and adaptation to enhance prediction over heterogeneous river segments. To reduce performance disparities across river segments, we introduce a centralized coordination strategy that adjusts training priorities for segments. We evaluate the prediction of water temperature within the Delaware River Basin, and the prediction of streamflow using simulated data from U.S. National Water Model in the Houston River network. The results showcase improvements in predictive performance and highlight the proposed model's ability to maintain spatial fairness over different river segments. 

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3. Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary

   https://doi.org/10.3390/w16141998  

   Gachloo, Mina; Liu, Qianqian; Song, Yang; Wang, Guozhi; Zhang, Shuhao; Hall, Nathan (July 2024, Water)

   This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R2 value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results. 

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4. Estimating a Directed Tree for Extremes

   Ngoc Mai Tran, Johannes Buck (October 2023, Journal of the Royal Statistical Society Series B Methodological)

   The Extremal River Problem has emerged as a flagship problem for causal discovery in extreme values of a network. The task is to recover a river network from only extreme flow measured at a set V of stations, without any information on the stations' locations. We present QTree, a new simple and efficient algorithm to solve the Extremal River Problem that performs very well compared to existing methods on hydrology data and in simulations. QTree returns a root-directed tree and achieves almost perfect recovery on the Upper Danube network data, the existing benchmark data set, as well as on new data from the Lower Colorado River network in Texas. It can handle missing data, has an automated parameter tuning procedure, and runs in time O(n |V|^2), where n is the number of observations and |V| the number of nodes in the graph. Furthermore, we prove that the QTree estimator is consistent under a Bayesian network model for extreme values with noise. We also assess the small sample behaviour of QTree through simulations and detail the strengths and possible limitations of QTree. 

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5. A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

   https://doi.org/10.1002/hyp.14619  

   Balson, Tyler; Ward, Adam S. (June 2022, Hydrological Processes)

   Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated. 

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