There is a strong interest in the climate community to improve the ability to accurately predict future trends of climate variables. Recently, machine learning methods have proven their ability to contribute to more accurate predictions of historical data on a variety of climate variables. There is also a strong interest in using statistical downscaling to predict local station data from the output of multi-model ensembles. This project looks at using the machine learning algorithm XGBoost and evaluating its ability to accurately predict historical monthly precipitation, with a focus of applying this method to simulate future precipitation trends. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26132 |
| Date | January 2020 |
| Creators | Hadzi-Tosev, Milena |
| Contributors | McNicholas, Dr. Paul D., Li, Dr. Zoe, Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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