H025
Advances in Water Quality Prediction
Session ID#: 248845
Session Description:
Increasing data availability, process understanding, and computational resources continue to enable advances in water quality prediction. Yet challenges remain to ensure reliable predictions across a range of hydrologic conditions and human impacts at spatial and temporal scales that are relevant to management decisions. We welcome presentations that provide data-driven approaches to surface and groundwater quality prediction, including:1) prediction under different hydrologic conditions and extremes,2) prediction for varying degrees of hydrologic alteration or land surface management,3) simultaneous multi-constituent prediction, 4) integrating surface and groundwater quality predictions,5) integrating process-based and machine learning approaches to guide predictions6) evaluating how models scale in space and time, including long-term projections and short-term forecasts,7) novel water quality model diagnostics or explainable AI for functional performance evaluation, and8) presentations related to the AGU2024 theme of “Where Science Connects Us” that provide perspectives on the water quality prediction community.
Index Terms:
1831 Groundwater quality [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
1910 Data assimilation, integration and fusion [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Jared David Smith, U.S. Geological Survey, Water Resources Mission Area, Reston, United States
Conveners: Janet Barclay, United States Geological Survey, Water Resources Mission Area, East Hartford, United States, Joel Blomquist, U.S. Geological Survey, Integrated Modeling and Prediction Division, Baltimore, United States and Dylan Blaskey, University of South Carolina, School of the Earth, Ocean & Environment, Columbia, United States
Hybrid Machine Learning Models for Forecasting Nitrate Concentrations at Watershed Scale – Case Study of the Delphos Watershed, Ohio (1879518)
Marjan Kordani1, Fariba Babaian1, Asmita Murumkar2, Heather Raymond3 and Vinayak S. Shedekar1, (1)The Ohio State University, Department of Food, Agricultural and Biological Engineering, Columbus, United States, (2)The Ohio State University, Department of Extension, Columbus, United States, (3)The Ohio State University, College of Food, Agriculture, and Environmental Science, Columbus, United States
Prediction of Groundwater Quality Using a Regression Machine Learning-based Framework for an Agricultural Watershed (1961688)
Ahmed Elsayed, University of Guelph, Guelph, ON, Canada, Jana Levison, G360 Institute for Groundwater Research, University of Guelph, Guelph, ON, Canada, Andrew D. Binns, University of Guelph, School of Engineering, Guelph, ON, Canada, Marie Larocque, Univ du Quebec - Montreal, Dorval, QC, Canada and Pradeep Goel, Ministry of the Environment, Conservation, and Parks, Environmental Monitoring and Reporting, Etobicoke, Canada
A Dynamic SPARROW Model to Assess Wetland Connectivity Impacts on Total Phosphorus Loads in the Canadian Side of Lake Erie Basin (1974652)
Odai Al Balasmeh, University of Toronto, Department of Physical and Environmental Sciences, Toronto, Canada, Akunne Okoli, University of Toronto, Department of Physical and Environmental Sciences, Toronto, ON, Canada, Alex Neumann, University of Toronto, Physical and Environmental Sciences, Toronto, ON, Canada and George B Arhonditsis, University of Toronto Scarborough, Department of Physical and Environmental Sciences, Toronto, ON, Canada
Modeling Detroit Reservoir water quality sensitivity in response to climate-driven change projected for Willamette Valley (1984463)
Emily J. Summers, USACE-ERDC, Vicksburg, United States, Jodi L. Ryder, US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, United States and Travis A Dahl, U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States
Spatio‑temporal and dynamic prediction of water quality changes (turbidity, chlorophyll-a and colour dissolved organic matter) using multi‑sensor data fusion: satellite, UAV, and cloud‑based modeling (1995384)
Abhinav Galodha, Indian Institute of Technology Delhi, School of Interdisciplinary Research (SIRe), New Delhi, India, Maria Peppa, Newcastle University, School of Engineering (SoE), Tyne and Wear, United Kingdom, Sam Wilson, Newcastle University, School of Natural and Environmental Sciences (SNES), Tyne and Wear, United Kingdom, Sanya Anees Assi, Netaji Subhas University of Technology (NSUT), Department of Electronics and Communications Engineering (DECE), Delhi, India, Brejesh Lall Prof, Indian Institute of Technology Delhi, Department of Electrical Engineering, New Delhi, India and Shaikh Ziauddin Ahammad Prof, Indian Institute of Technology Delhi, Department of Biochemical Engineering and Biotechnology, New Delhi, India
Influence of Hydrological Variability and Wetland Connectivity on Total Phosphorus Loads in Lake Erie- a Hierarchical SPARROW Modelling Approach (1996243)
Akunne Okoli, University of Toronto, Department of Physical and Environmental Sciences, Toronto, ON, Canada, Odai Al Balasmeh, University of Toronto, Department of Physical and Environmental Sciences, Toronto, Canada, Alex Neumann, University of Toronto, Physical and Environmental Sciences, Toronto, ON, Canada and George B Arhonditsis, University of Toronto Scarborough, Department of Physical and Environmental Sciences, Toronto, ON, Canada
Data-Driven Modelling of Heavy Metal Contamination in Groundwater Using Supervised and Unsupervised Machine Learning Algorithms. (2001719)
Adarsh Krishna K R, Vellore Institute of Technology, School of Advanced Sciences, Chennai, India, Renjith P N, Vellore Institute of Technology Chennai, School of Computer Science and Engineering, Chennai, India and Sarath K V, University of Kerala, Department of Geology, Trivandrum, India
