1. Ahmed, S.N., Moltz, H.L.N., Schultz, C.L., and Seck, A., 2020, 2020 Washington metropolitan area water supply study—Demand and resource availability forecast for the year 2050: Interstate Commission on the Potomac River Basin report 20-3, 167 p.
2. Belitz, K., and Stackelberg, P.E., 2021, Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models: Environmental Modelling & Software, v. 139, 12 p., accessed May 04, 2023, at https://doi.org/10.1016/j.envsoft.2021.105006.
3. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J., 1984, Classification and regression trees: New York, Chapman & Hall/CRC, 368 p. [Also available at https://doi.org/10.1201/9781315139470.]
4. Chamberlin, C.A., 2024, Model archive, input data, modeled estimates of water use 2005-2021, and forecasts of water use in 2030 and 2040 in Providence, Rhode Island: U.S. Geological Survey data release, https://doi.org/10.5066/P94XIQ7W.
5. Chen, T., and Guestrin, C., 2016, XGBoost: A scalable tree boosting system: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Calif., August 13–17, 2016: Association for Computing Machinery, p. 785–794, accessed September 6, 2023, at https://doi.org/10.1145/2939672.2939785.