Journal Articles, Conference Papers, and Presentations

Liu T, Deng Q, Ding K, Sabo JL, Liu H, Candan KS, Muenich RL. Applying Graph Neural Networks to Improve the Data Resolution of Stream Water Quality Monitoring Networks. American Geophysical Union Annual Meeting, December 12-15, 2023, San Francisco, CA.

Villarreal D, Liu T, Deng Q, Steissberg T, Sabo JL, Muenich RL. Do upland agricultural areas and downstream reservoirs counteract efforts to improve water resources? American Geophysical Union Annual Meeting, December 12-15, 2023, San Francisco, CA

Muenich RL. Changing the Paradigm of Water Quality Modeling in the Age of Big Data and Machine Learning. Presentation @ Arkansas Water Resources Conference, July 18-20, 2023. Fayetteville, AR

Steissberg TE, Muenich RL.  Integrating water resources infrastructure with upland management to advance nature-based solutions for water quantity and quality. International Soil and Water Assessment Tool Conference, June 26-30, 2023, Aarhus, Denmark (see the following link for the corresponding NEWN presentation)

Shah R, Tsai Y, Stampoulis D, Damavandi H G and Sabo J 2023, Design principles for engineering wetlands to improve resilience of coupled built and natural water infrastructure. Environ. Res. Lett. 18 114045

Huang, L. and J. Sabo (2023). "Physics Informed Machine Learning for modeling groundwater levels." American Geophysical Union (AGU) Fall Meeting.

Paras Sheth, Ahmadreza Mosallanezhad, Kaize Ding, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan. STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction. CIKM 2023: 4815-4821

Pratanu Mandal , Bilgehan Arslan , Paras Sheth , Rebecca Muenich , K. Selcuk Candan. Streamflow Prediction using SpatioTemporal Deep-Learning based Framework with Reinforcement Learning. HydroML poster, May 22-24, 2023, Berkeley, California.

Deng Q, Liu T, Sheth P, Muenich RL, Sabo J. Time-Space Transformer for Agriculture Evaluation Applied to Streamflow Simulation. HydroML Symposium, May 22-24, 2023, Berkeley, California

F. Azad et al. A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning. ACM Transaction on Spatial Algorithms and Systems, Accepted for publication, 2024.

Shu Wan, Reepal Shah, Qi Deng, John Sabo, Huan Liu, K. Selcuk Candan, Spatiotemporal Causal Learning for Streamflow Forecasting, poster at the N-EWN Symposium, 2024.

P. Mandal, Y. Choi, R. Shah, J. Sabo, H. Liu. K.S. Candan. Identifying Potential Wetlands via Causality-based Data Imputation and Knowledge Transfer., presentation at the N-EWN Symposium, 2024.