M.S. Applied Data Science - Capstone Chronicles 2025

21

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 California State Water Resources Control Board. (2024). California drinking water quality database. https://data.cnra.ca.gov/dataset/drinking-water -quality Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785 Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16(12), 1957. https://doi.org/10.3390/s16121957 Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. (2001). Analysis of time series structure: SSA and related techniques. Chapman & Hall/CRC. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2

Helsel, D. R. (2005). Nondetects and data analysis: Statistics for censored environmental data. Wiley. https://doi.org/10.1002/0471725183 Helsel, D. R. (2012). Statistics for censored environmental data using Minitab® and R (2nd ed.). Wiley. https://doi.org/10.1002/9781118136184 Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55 Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. Lund, R., & Reeves, J. (2002). Detection of undocumented changepoints: A revision of the two-phase regression model. Journal of Climate, 15(3), 2547–2554. https://doi.org/10.1175/1520-0442(2002)015< 2547:DOUCM>2.0.CO;2 McKinney, W. (2010). Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference (pp. 51–56). https://doi.org/10.25080/Majora-92bf1922-00 a Mohanty, I., & Rickwood, L. (2020). Handling missing data in time series: A comparative study. Journal of Time Series Analysis, 41(4), 567–586. https://doi.org/10.1111/jtsa.12510 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning

93