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Activity Number:
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268
- Extreme Machine Learning Methods and Applications: Domestic and International
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Statistical Consulting
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Abstract #312857
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Title:
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Empirical data-fusion approaches to generate model covariates
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Author(s):
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Luca Sartore* and Jake Abernethy and Claire Boryan and Lu Chen and Kevin Hunt and CLIFFORD H SPIEGELMAN and Linda J Young
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Companies:
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National Institute of Statistical Sciences and NASS and USDA and USDA and USDA and Texas A&M University and NASS
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Keywords:
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Non-parametric;
Machine Learning;
Empirical estimates;
Agricultural statistics;
Artificial covariates
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Abstract:
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USDA's National Agricultural Statistics Service (NASS) publishes more than 500 reports every year including prospective planting estimates and planted acreage estimates for major crops at the state level. To produce these estimates, several surveys are conducted before and during the growing season. To enhance the accuracy of these estimates, NASS has investigated the use of a model that incorporates external sources of information such as soil type, weather and remote sensing data obtained at different temporal and spatial resolutions. Estimates are updated weekly as the input data evolve over time. Empirical approaches based on an ensemble of non-parametric techniques are investigated to generate a set of covariates by fusing together several sources of information. The resulting variables are then evaluated on two different tasks: the classification of prevented planting fields, and the performance in predicting crop at the field level. A case study using historical data in the state of Illinois is presented.
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Authors who are presenting talks have a * after their name.
