- By:
- Tubbs, Heidi C; Anyamba, Assaf
- Page Number:
- 90-95
- Book Title:
- 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP)
- Publication Date:
- December 22, 2022
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- International Conference on Big Data Analytics and Practices (IBDAP)
- Conference Location:
- Bangkok, Thailand
- Conference Sponsor:
- Various
- View DOI Listing:
- https://doi.org/10.1109/IBDAP55587.2022.9907717
Abstract
This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R 2 value of 0.9991501 with an adjusted R 2 value of 0.9991502 and a ridge regression model with an R 2 value of 0.9865443 with an adjusted R 2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations.
