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Hydro-Climatological Summer Trends In The Continental United States, Naya Mairena Flores
Hydro-Climatological Summer Trends In The Continental United States, Naya Mairena Flores
REU Final Reports
We investigated trends in air temperature, stream temperature and discharge for rivers across the continental United States from the summer months of 1996 to 2016. Using GAGES II from USGS and PRISM and programming language R we analyzed specific hydrological trends in Mann-Kendall’s tests. After collecting the slope values whether they were negative or positive and the P-Values, the significance of that slope, we mapped slopes of trends in GIS. Stream temperature increased 12% of stations across the summer, while air temperature increased 22% of stations, and discharge decreased 15% of stations, respectively. Seven day moving average of daily maximum …
Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn
Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn
REU Final Reports
The objective of this research is to explore prediction accuracy of water quality factors, with techniques and algorithms in machine learning consisting of a variation of support vector machines - Support Vector Regression (SVR) and the gradient boosting algorithm Extreme Gradient Boosting (XGBoost). Both the XGBoost and SVR algorithms were used to predict nine different factors with success rates ranging from 79% to 99%. Parameters of these algorithms were also explored to test the prediction accuracy levels of individual water quality factors. These parameters included normalizing the data, filling missing data points, and training and testing on a large set …