Aquatic Sciences: Modeling Quality of Water in Lakes​

The goal of this project is to develop hybrid-ecology-ML models of lake water quality where some lake components are represented using ecology models while others are represented using KGML models. We aim to use KGML to improve the accuracy of current standards in lake modeling as well as to discover new knowledge of lake physics and system interactions. This is in collaboration with researchers from BIO at VT, and limnologists from Univ. of Wisconsin.

Papers:

Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process-Based Modeling With Deep Learning

R. Ladwig, A. Daw, E. A. Albright, C. Buelo, A. Karpatne, M. F. Meyer, A. Neog, P. C. Hanson, H. A. Dugan

Journal of Advances in Modeling Earth Systems (JAMES), 2023

Paper | Github

Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

Arka Daw, Anuj Karpatne, William D. Watkins, Jordan S. Read, Vipin Kumar

Knowledge Guided Machine Learning, 2022

Chapter | Github

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne

SIAM International Conference on Data Mining, 2020

Paper | Github