Statistical modeling of local precipitation involves understanding local, regional and global factors informative of precipitation variability in a region. Modern machine learning methods for feature selection can potentially be explored for identifying statistically significant features from pool of potential predictors of precipitation. In this work, we consider sparse regression, which simultaneously performs feature selection and regression, followed by random permutation tests for selecting dominant factors. We consider average winter precipitation over Great Lakes Region in order to identify its dominant influencing factors. Experiments show that global climate indices, computed at different temporal lags, offer predictive information for winter precipitation. Further, among the dominant factors identified using randomized permutation tests, multiple climate indices indicate the influence of geopotential height patterns on winter precipitation. Using composite analysis, we illustrate that certain patterns are indeed typical in high and low precipitation years, and offer plausible scientific reasons for variations in precipitation. Thus, feature selection methods can be useful in identifying influential climate processes and variables, and thereby provide useful hypotheses over physical mechanisms affecting local precipitation.
|Original language||English (US)|
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||7|
|State||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: Feb 12 2016 → Feb 17 2016
|Name||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Other||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||2/12/16 → 2/17/16|
Bibliographical noteFunding Information:
We would like to thank Debasish Das for help with the climate data. This research was supported in part by NSF Grants IIS-1029711, IIS-0916750, SES-0851705, IIS- 0812183, and NSF CAREER Grant IIS-0953274. We are grateful for technical support from University of Minnesota Supercomputing Institute (MSI).
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