Developed a Jupyter notebook in Python to predict high-cost water quality metrics (DO and NO3 + NO2 concentration) from low-cost metrics (specific conductance, temperature, and discharge) using data from the USGS NWIS. Searched for and gathered historical data w/ the desired parameters from two sites in the Yakima River Basin to train a NN model in Keras. Naive C-q and time series plots revealed a strong correlation between the low- and high-cost parameters. The trained model had accuracies (R^2) ranging from 0.68 - 0.93. Created an official PNNL Information Release and presented these findings at the PNNL August intern research symposium. Official title: Science Programs Intern - High School SRAP - Earth Systems Measurements and Data Intern.